Playwright Agents — 🎭 Planner, 🎭 Generator, 🎭 Healer

What are Playwright Agents?

This article distills the official guidance and demo video into a practical, production‑ready walkthrough. Playwright ships three agents you can run independently or in a loop: 🎭 Planner, 🎭 Generator, and 🎭 Healer.

🎭 Planner

Explores your app and produces a human‑readable Markdown plan.

  • Input: a clear request (e.g. “Generate a plan for guest checkout”), a seed test, optional PRD.
  • Output: specs/*.md with scenarios, steps, and expected results.

🎭 Generator

Converts the Markdown plan into executable Playwright tests and validates selectors/assertions during generation.

  • Input: Markdown from specs/, seed test and fixtures.
  • Output: tests/*.spec.ts aligned to the plan.

🎭 Healer

Runs tests, replays failures, proposes patches (locator updates, waits, data fixes) and re‑runs until passing or guardrails stop.

  • Input: failing test name.
  • Output: a passing test or a skipped test if functionality is broken.
🎭 Planner → 🎭 Generator → 🎭 Healer Overview

1. Requirements

  • Node.js 18+ and npm
  • Playwright Test latest version
  • VS Code 1.105+ (Insiders channel) for full agentic UI experience
  • AI Assistant – Choose one: Claude Code, OpenCode, or VS Code with AI extensions
  • Git for version control
  • Modern web browser (Chrome, Firefox, Safari)

2. Step-by-Step Installation Guide

Step 1: Prerequisites

  • Install Node.js 18+ from nodejs.org
  • Install npm (comes with Node.js)
  • Install VS Code 1.105+ from VS Code Insiders for agentic experience
  • Choose and install an AI Assistant:
    • Claude Code – for Claude integration
    • OpenCode – for OpenAI integration
    • VS Code with AI extensions – for built-in AI features
  • Install Git for version control

Step 2: Navigate to Demo Directory

# Navigate to the demo directory
C:\Users\ADMIN\Documents\AI_QUEST_LTP> cd "playwright Agent Test Example - PhatLT"

Step 3: Install Dependencies

playwright Agent Test Example - PhatLT> npm install
playwright Agent Test Example - PhatLT> npx playwright install

Step 4: Initialize Playwright Agents

# Initialize agent definitions for Claude Code (recommended)
playwright Agent Test Example - PhatLT> npx playwright init-agents --loop=claude

# Or for VS Code
playwright Agent Test Example - PhatLT> npx playwright init-agents --loop=vscode

# Or for OpenCode
playwright Agent Test Example - PhatLT> npx playwright init-agents --loop=opencode

Step 5: Verify Setup

# Test seed file
playwright Agent Test Example - PhatLT> npx playwright test tests/seed-agents.spec.ts

# Check project structure
playwright Agent Test Example - PhatLT> dir .claude\agents
playwright Agent Test Example - PhatLT> dir .github
playwright Agent Test Example - PhatLT> dir specs
playwright Agent Test Example - PhatLT> npm init -y
Wrote to playwright Agent Test Example - PhatLT\package.json:
{
  "name": "phatlt-playwright",
  "version": "1.0.0",
  "main": "index.js",
  "scripts": {
    "test": "playwright test",
    "test:headed": "playwright test --headed",
    "test:ui": "playwright test --ui",
    "test:debug": "playwright test --debug",
    "test:chromium": "playwright test --project=chromium",
    "test:firefox": "playwright test --project=firefox",
    "test:webkit": "playwright test --project=webkit",
    "report": "playwright show-report",
    "codegen": "playwright codegen"
  },
  "keywords": [],
  "author": "",
  "license": "ISC",
  "type": "commonjs",
  "description": "",
  "devDependencies": {
    "@playwright/test": "^1.56.0",
    "@types/node": "^24.7.2"
  }
}

playwright Agent Test Example - PhatLT> npm install -D @playwright/test
added 1 package, and audited 2 packages in 2s
found 0 vulnerabilities

playwright Agent Test Example - PhatLT> npx playwright install
Installing browsers...
✓ Chromium 120.0.6099.109
✓ Firefox 120.0
✓ WebKit 17.4

playwright Agent Test Example - PhatLT> npx playwright init
✓ Created playwright.config.ts
✓ Created tests/
✓ Created tests/example.spec.ts
✓ Created tests/seed.spec.ts

3. Step-by-Step Testing Guide

Step 1: Test Seed File

Run the seed test to verify Playwright Agents setup:

# Test seed file for agents
playwright Agent Test Example - PhatLT> npx playwright test tests/seed-agents.spec.ts

# Run with browser UI visible
playwright Agent Test Example - PhatLT> npx playwright test tests/seed-agents.spec.ts --headed

# Run in debug mode
playwright Agent Test Example - PhatLT> npx playwright test tests/seed-agents.spec.ts --debug

Step 2: Test Generated Tests

Run the example generated tests from the Generator agent:

# Run generated Google search tests
playwright Agent Test Example - PhatLT> npx playwright test tests/google-search-generated.spec.ts

# Run specific test by name
playwright Agent Test Example - PhatLT> npx playwright test --grep "Perform Basic Search"

# Run all tests
playwright Agent Test Example - PhatLT> npx playwright test

Step 3: Test Different Browsers

# Run tests only on Chromium
playwright Agent Test Example - PhatLT> npx playwright test --project=chromium

# Run tests only on Firefox
playwright Agent Test Example - PhatLT> npx playwright test --project=firefox

# Run tests only on WebKit
playwright Agent Test Example - PhatLT> npx playwright test --project=webkit

Step 4: Generate Test Reports

# Generate HTML report
playwright Agent Test Example - PhatLT> npx playwright show-report

# Run tests with UI mode
playwright Agent Test Example - PhatLT> npx playwright test --ui

Step 5: Using Playwright Agents

Now you can use the Playwright Agents workflow with Claude Code:

# In Claude Code, ask the Planner:
"I need test scenarios for Google search functionality. Use the planner agent to explore https://www.google.com"

# Then ask the Generator:
"Use the generator agent to create tests from the test plan in specs/"

# Finally, use the Healer if tests fail:
"The test 'Perform Basic Search' is failing. Use the healer agent to fix it."

4. Project Structure and Files

playwright Agent Test Example - PhatLT/
├── .claude/agents/              # Claude Code agent definitions
│   ├── playwright-test-planner.md    # 🎭 Planner agent
│   ├── playwright-test-generator.md  # 🎭 Generator agent
│   └── playwright-test-healer.md     # 🎭 Healer agent
├── .github/                     # Official agent definitions
│   ├── planner.md               # 🎭 Planner instructions
│   ├── generator.md             # 🎭 Generator instructions
│   └── healer.md                # 🎭 Healer instructions
├── specs/                       # Test plans (Markdown)
│   └── google-search-operations.md   # Example test plan
├── tests/                       # Generated tests
│   ├── seed-agents.spec.ts      # Seed test for agents
│   └── google-search-generated.spec.ts  # Generated test example
├── .mcp.json                    # MCP server configuration
├── playwright.config.ts         # Playwright configuration
├── package.json                 # Project dependencies
└── test-results/               # Test execution results

5. How Playwright Agents Work (End‑to‑End)

  1. 🎭 Planner — explores your app and creates human-readable test plans saved in specs/ directory.
  2. 🎭 Generator — transforms Markdown plans into executable Playwright tests in tests/ directory.
  3. 🎭 Healer — automatically repairs failing tests by updating selectors and waits.
  4. Execution — run generated tests with npx playwright test.
  5. Maintenance — Healer fixes issues automatically, keeping tests stable over time.
playwright Agent Test Example - PhatLT> npx playwright test tests/seed-agents.spec.ts

Running 1 test using 1 worker

  ✓ [chromium] › tests/seed-agents.spec.ts › seed (2.1s)

  1 passed (2.1s)

playwright Agent Test Example - PhatLT> npx playwright test tests/google-search-generated.spec.ts

Running 5 tests using 1 worker

  ✓ [chromium] › tests/google-search-generated.spec.ts › Google Search - Basic Operations › Perform Basic Search (3.2s)
  ✓ [chromium] › tests/google-search-generated.spec.ts › Google Search - Basic Operations › Verify Search Box Functionality (1.8s)
  ✓ [chromium] › tests/google-search-generated.spec.ts › Google Search - Basic Operations › Search with Empty Query (1.5s)
  ✓ [chromium] › tests/google-search-generated.spec.ts › Google Search - Results Validation › Verify Search Results Display (4.1s)
  ✓ [chromium] › tests/google-search-generated.spec.ts › Google Search - Results Validation › Navigate Through Search Results (5.3s)

  5 passed (16.0s)

6. How Playwright Agents Work

Playwright Agents follow a structured workflow as described in the official documentation. The process involves three main agents working together:

🎭 Planner Agent

The Planner explores your application and creates human-readable test plans:

  • Input: Clear request (e.g., “Generate a plan for guest checkout”), seed test, optional PRD
  • Output: Markdown test plan saved as specs/basic-operations.md
  • Process: Runs seed test to understand app structure and creates comprehensive test scenarios

🎭 Generator Agent

The Generator transforms Markdown plans into executable Playwright tests:

  • Input: Markdown plan from specs/
  • Output: Test suite under tests/
  • Process: Verifies selectors and assertions live, generates robust test code

🎭 Healer Agent

The Healer automatically repairs failing tests:

  • Input: Failing test name
  • Output: Passing test or skipped test if functionality is broken
  • Process: Replays failing steps, inspects UI, suggests patches, re-runs until passing
// Example: Generated test from specs/basic-operations.md
// spec: specs/basic-operations.md
// seed: tests/seed.spec.ts

import { test, expect } from '../fixtures';

test.describe('Adding New Todos', () => {
  test('Add Valid Todo', async ({ page }) => {
    // 1. Click in the "What needs to be done?" input field
    const todoInput = page.getByRole('textbox', { name: 'What needs to be done?' });
    await todoInput.click();

    // 2. Type "Buy groceries"
    await todoInput.fill('Buy groceries');

    // 3. Press Enter key
    await todoInput.press('Enter');

    // Expected Results:
    // - Todo appears in the list with unchecked checkbox
    await expect(page.getByText('Buy groceries')).toBeVisible();
    const todoCheckbox = page.getByRole('checkbox', { name: 'Toggle Todo' });
    await expect(todoCheckbox).toBeVisible();
    await expect(todoCheckbox).not.toBeChecked();

    // - Counter shows "1 item left"
    await expect(page.getByText('1 item left')).toBeVisible();

    // - Input field is cleared and ready for next entry
    await expect(todoInput).toHaveValue('');
    await expect(todoInput).toBeFocused();

    // - Todo list controls become visible
    await expect(page.getByRole('checkbox', { name: '❯Mark all as complete' })).toBeVisible();
  });
});

7. Agent Deep Dives

🎭 Planner — author plans that generate great tests

  • Goal: Convert product intent into executable, atomic scenarios.
  • Inputs: business request, seed.spec.ts, optional PRD/acceptance criteria.
  • Output quality tips: prefer user‑intent over UI steps, keep 1 scenario = 1 assertion focus, name entities consistently.
  • Anti‑patterns: mixing setup/teardown into steps; over‑specifying selectors in Markdown.

🎭 Generator — compile plans into resilient tests

  • Validates selectors live: uses your running app to confirm locators/assertions.
  • Structure: mirrors specs/*.md; adds fixtures from seed.spec.ts; keeps tests idempotent.
  • Resilience: prefer roles/labels; avoid brittle CSS/XPath; centralize waits.

🎭 Healer — stabilize and protect correctness

  • Scope: flaky selectors, timing, deterministic data; not business‑logic rewrites.
  • Review gates: patches proposed as diffs; you accept/reject before merge.
  • Outcomes: test fixed, or skipped with a documented reason when the feature is broken.

8. Project Structure and Artifacts

Playwright Agents follow a structured approach as described in the official documentation. The generated files follow a simple, auditable structure:

repo/
  .github/                    # agent definitions
    planner.md               # planner agent instructions
    generator.md             # generator agent instructions  
    healer.md                # healer agent instructions
  specs/                     # human-readable test plans
    basic-operations.md      # generated by planner
  tests/                     # generated Playwright tests
    seed.spec.ts             # seed test for environment
    add-valid-todo.spec.ts   # generated by generator
  playwright.config.ts       # Playwright configuration

Agent Definitions (.github/)

Under the hood, agent definitions are collections of instructions and MCP tools provided by Playwright. They should be regenerated whenever Playwright is updated:

# Initialize agent definitions
npx playwright init-agents --loop=vscode
npx playwright init-agents --loop=claude  
npx playwright init-agents --loop=opencode

Specs in specs/

Specs are structured plans describing scenarios in human-readable terms. They include steps, expected outcomes, and data. Specs can start from scratch or extend a seed test.

Tests in tests/

Generated Playwright tests, aligned one-to-one with specs wherever feasible. Generated tests may include initial errors that can be healed automatically by the healer agent.

Seed tests (seed.spec.ts)

Seed tests provide a ready-to-use page context to bootstrap execution. The planner runs this test to execute all initialization necessary for your tests including global setup, project dependencies, and fixtures.

// Example: seed.spec.ts
import { test, expect } from './fixtures';

test('seed', async ({ page }) => {
  // This test uses custom fixtures from ./fixtures
  // 🎭 Planner will run this test to execute all initialization
  // necessary for your tests including global setup, 
  // project dependencies and all necessary fixtures and hooks
});

9. Examples from Official Documentation

🎭 Planner Output Example

The 🎭 Planner generates human-readable test plans saved as specs/basic-operations.md:

# TodoMVC Application - Basic Operations Test Plan

## Application Overview

The TodoMVC application is a React-based todo list manager that demonstrates 
standard todo application functionality. Key features include:

- **Task Management**: Add, edit, complete, and delete individual todos
- **Bulk Operations**: Mark all todos as complete/incomplete and clear all completed todos  
- **Filtering System**: View todos by All, Active, or Completed status with URL routing support
- **Real-time Counter**: Display of active (incomplete) todo count
- **Interactive UI**: Hover states, edit-in-place functionality, and responsive design

## Test Scenarios

### 1. Adding New Todos

**Seed:** `tests/seed.spec.ts`

#### 1.1 Add Valid Todo

**Steps:**
1. Click in the "What needs to be done?" input field
2. Type "Buy groceries"
3. Press Enter key

**Expected Results:**
- Todo appears in the list with unchecked checkbox
- Counter shows "1 item left"
- Input field is cleared and ready for next entry
- Todo list controls become visible (Mark all as complete checkbox)

🎭 Generator Output Example

The 🎭 Generator transforms the Markdown plan into executable Playwright tests:

// Generated test from specs/basic-operations.md
// spec: specs/basic-operations.md
// seed: tests/seed.spec.ts

import { test, expect } from '../fixtures';

test.describe('Adding New Todos', () => {
  test('Add Valid Todo', async ({ page }) => {
    // 1. Click in the "What needs to be done?" input field
    const todoInput = page.getByRole('textbox', { name: 'What needs to be done?' });
    await todoInput.click();

    // 2. Type "Buy groceries"
    await todoInput.fill('Buy groceries');

    // 3. Press Enter key
    await todoInput.press('Enter');

    // Expected Results:
    // - Todo appears in the list with unchecked checkbox
    await expect(page.getByText('Buy groceries')).toBeVisible();
    const todoCheckbox = page.getByRole('checkbox', { name: 'Toggle Todo' });
    await expect(todoCheckbox).toBeVisible();
    await expect(todoCheckbox).not.toBeChecked();

    // - Counter shows "1 item left"
    await expect(page.getByText('1 item left')).toBeVisible();

    // - Input field is cleared and ready for next entry
    await expect(todoInput).toHaveValue('');
    await expect(todoInput).toBeFocused();

    // - Todo list controls become visible
    await expect(page.getByRole('checkbox', { name: '❯Mark all as complete' })).toBeVisible();
  });
});

10. Best Practices

  • Keep plans atomic: Small, focused scenarios help 🎭 Generator produce clean tests. Avoid mixing multiple user flows in one scenario.
  • Stabilize with seed: Centralize navigation, authentication, and data seeding in seed.spec.ts to ensure consistent test environment.
  • Prefer semantic selectors: Use getByRole, getByLabel, and getByText for resilient element selection.
  • 🎭 Healer guardrails: Review patches carefully; accept locator/wait tweaks, but avoid broad logic changes that might mask real bugs.
  • Version agent definitions: Commit .github/ changes and regenerate them whenever Playwright is updated.
  • Choose the right AI assistant: VS Code, Claude Code, or OpenCode — pick the one that fits your team’s workflow and preferences.
  • Maintain traceability: Keep clear 1:1 mapping from specs/*.md to tests/*.spec.ts using comments and headers.
  • Test the agents: Start with simple scenarios to understand how each agent works before tackling complex user flows.

11. Troubleshooting

🎭 Planner can’t explore the app

Ensure your app is running locally, seed test works, and the app is accessible. Check that authentication and navigation are properly set up in seed.spec.ts.

🎭 Generator can’t find elements

Run the app locally, ensure routes are correct, and verify that elements have proper roles, labels, or accessible names. The 🎭 Generator validates selectors live against your running app.

🎭 Healer loops without fixing

Set explicit timeouts, add deterministic test data, and reduce flakiness in network waits. The 🎭 Healer works best with stable, predictable test conditions.

AI assistant doesn’t trigger agents

Re-run npx playwright init-agents --loop=[assistant], reload the IDE, and ensure the correct workspace root is open with agent definitions in .github/.

Generated tests fail immediately

Check that your seed test passes first. Ensure the app state matches what the 🎭 Planner observed. Verify that test data and authentication are consistent between planning and execution.

Agent definitions are outdated

Regenerate agent definitions after Playwright updates: npx playwright init-agents --loop=[assistant]. This ensures you have the latest tools and instructions.

12. CI/CD Integration

You can run the same agent‑generated tests in CI. Keep agent definitions in the repo and refresh them on Playwright upgrades.

# .github/workflows/tests.yml (excerpt)
name: Playwright Tests
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
      - run: npm ci
      - run: npx playwright install --with-deps
      - run: npx playwright test --reporter=html

13. FAQ

Do I need Claude Code?

No. Playwright Agents work with VS Code (v1.105+), Claude Code, or OpenCode. Choose the AI assistant that fits your team’s workflow and preferences.

Where do test plans live?

In specs/ as Markdown files generated by the 🎭 Planner. Generated tests go to tests/.

What if a feature is actually broken?

The 🎭 Healer can skip tests with an explanation instead of masking a real bug. It distinguishes between flaky tests and genuinely broken functionality.

Can I run agent-generated tests in CI?

Yes. The agents produce standard Playwright tests that run with npx playwright test in CI. Agent definitions are only needed for test authoring, not execution.

How do I update agent definitions?

Run npx playwright init-agents --loop=[assistant] whenever Playwright is updated to get the latest tools and instructions.

What’s the difference between 🎭 Planner, 🎭 Generator, and 🎭 Healer?

🎭 Planner: Explores your app and creates human-readable test plans. 🎭 Generator: Transforms plans into executable Playwright tests. 🎭 Healer: Automatically fixes failing tests by updating selectors and waits.

14. Demo video and Source code

GitHubGitHub repository: phatltscuti/playwright_agents

 

Context Engineering cho AI Agents – Tóm tắt từ Anthropic

 

Context Engineering cho AI Agents

Tóm tắt từ bài viết của Anthropic về nghệ thuật quản lý context trong phát triển AI

🎯 Context Engineering là gì?

Context Engineering là tập hợp các chiến lược để tuyển chọn và duy trì bộ tokens (thông tin) tối ưu trong quá trình AI agents hoạt động.

Nó bao gồm việc quản lý toàn bộ trạng thái context như:

  • System prompts (hướng dẫn hệ thống)
  • Tools (công cụ)
  • Model Context Protocol (MCP)
  • External data (dữ liệu bên ngoài)
  • Message history (lịch sử hội thoại)
  • Các thông tin khác trong context window

💡 Bản chất: Context Engineering là nghệ thuật và khoa học về việc tuyển chọn thông tin nào sẽ đưa vào context window giới hạn từ vũ trụ thông tin liên tục phát triển của agent.

🔄 Khác biệt giữa Context Engineering và Prompt Engineering

📝 Prompt Engineering

  • Focus: Cách viết instructions (hướng dẫn)
  • Phạm vi: Tối ưu hóa system prompts
  • Use case: Tác vụ đơn lẻ, one-shot
  • Tính chất: Rời rạc, tĩnh

Ví dụ: “Tóm tắt văn bản này thành 3 điểm chú trọng số liệu tài chính”

🧠 Context Engineering

  • Focus: Model nhìn thấy gì trong context window
  • Phạm vi: Toàn bộ trạng thái thông tin
  • Use case: Multi-turn, tác vụ dài hạn
  • Tính chất: Lặp lại, động, liên tục

Ví dụ: Quyết định agent nên xem toàn bộ tài liệu, 3 phần cuối, hay bản tóm tắt đã chuẩn bị?

🎭 Ẩn dụ: Prompt engineering là “nói cho ai đó biết phải làm gì”, còn context engineering là “quyết định nên cung cấp nguồn lực gì cho họ”.

⚡ Tại sao Context Engineering quan trọng hơn?

Khi AI agents thực hiện các tác vụ phức tạp trên nhiều vòng lặp, chúng tạo ra ngày càng nhiều dữ liệu. Thông tin này phải được tinh chỉnh theo chu kỳ. Context engineering xảy ra mỗi khi chúng ta quyết định đưa gì vào model – đây là quá trình lặp đi lặp lại, không phải một lần.

⚠️ Những điều cần chú ý khi phát triển AI Agents

1. 🎯 Vấn đề “Goldilocks Zone” cho System Prompts

System prompts cần nằm ở “vùng vừa phải” giữa hai thái cực:

❌ Quá cứng nhắc: Hardcode logic if-else phức tạp → agent dễ vỡ, khó bảo trì

❌ Quá mơ hồ: Hướng dẫn chung chung, giả định context chung → thiếu tín hiệu cụ thể

✅ Vùng tối ưu: Đủ cụ thể để dẫn dắt hành vi, nhưng đủ linh hoạt để cung cấp heuristics mạnh mẽ

2. 🧹 “Context Rot” – Sự suy giảm độ chính xác

Khi context window dài ra, độ chính xác của model giảm xuống:

  • Giới hạn chú ý: LLMs giống con người – không thể nhớ mọi thứ khi quá tải. Nhiều tokens ≠ chính xác hơn
  • Context rot: Context càng dài, độ chính xác truy xuất càng giảm. Thêm 100 trang logs có thể che mất chi tiết quan trọng duy nhất
  • Kiến trúc transformer: Tạo n² mối quan hệ giữa các tokens (10K tokens = 100M quan hệ, 100K tokens = 10B quan hệ)

💡 Giải pháp: Implement pagination, range selection, filtering, truncation với giá trị mặc định hợp lý

3. 🔧 Quản lý Tools hiệu quả

  • Giữ tools riêng biệt: Không tạo 2 tools cùng làm việc giống nhau (VD: cùng fetch news)
  • Mô tả rõ ràng: Viết tool descriptions như hướng dẫn nhân viên mới – rõ ràng, tránh mơ hồ
  • Token-efficient: Giới hạn tool responses (VD: Claude Code giới hạn 25,000 tokens mặc định)
  • Error handling tốt: Error messages phải cụ thể, actionable, không phải error codes mơ hồ

4. 📊 Just-in-Time Context Retrieval

Thay vì load toàn bộ dữ liệu trước, hãy fetch dữ liệu động khi cần:

  • Tránh overload context window
  • Giảm token costs
  • Ngăn context poisoning (nhiễu thông tin)
  • Tương tự cách con người dùng hệ thống indexing bên ngoài

5. 🎨 Ba chiến lược cho tác vụ dài hạn

📦 Compaction (Nén thông tin)

Tóm tắt context cũ, giữ lại thông tin quan trọng

📝 Structured Note-Taking

Agent tự ghi chú có cấu trúc về những gì đã làm

🤖 Multi-Agent Architecture

Spawn sub-agents nhỏ cho các tác vụ hẹp, trả về kết quả ngắn gọn

6. 🎯 Ưu tiên Context theo tầm quan trọng

🔴 High Priority (luôn có trong context): Tác vụ hiện tại, kết quả tool gần đây, hướng dẫn quan trọng

🟡 Medium Priority (khi có không gian): Examples, quyết định lịch sử

⚪ Low Priority (on-demand): Nội dung file đầy đủ, documentation mở rộng

7. 📈 Monitoring và Iteration

Theo dõi liên tục:

  • Token usage per turn
  • Tool call frequency
  • Context window utilization
  • Performance ở các độ dài context khác nhau
  • Recall vs Precision khi rút gọn context

💡 Quy trình: Bắt đầu đơn giản → Test → Xác định lỗi → Thêm hướng dẫn cụ thể → Loại bỏ redundancy → Lặp lại

💡 Kết luận

Context engineering là kỹ năng then chốt để xây dựng AI agents hiệu quả. Khác với prompt engineering tập trung vào “cách viết instructions”, context engineering quan tâm đến “môi trường thông tin toàn diện” mà agent hoạt động.

Thành công không nằm ở việc tìm từ ngữ hoàn hảo, mà là tối ưu hóa cấu hình context để tạo ra hành vi mong muốn một cách nhất quán.

🎯 Nguyên tắc cốt lõi: Tìm bộ tokens nhỏ nhất có tín hiệu cao nhất để tối đa hóa khả năng đạt được kết quả mong muốn. Mỗi từ không cần thiết, mỗi mô tả tool thừa, mỗi dữ liệu cũ đều làm giảm hiệu suất agent.

Shipping with Codex

Codex: Kỹ Sư Phần Mềm AI Đã Tạo Ra “Sự Thay Đổi Cảm Hứng Lớn” (Vibe Shift) Trong Lập Trình

Gần đây, tại OpenAI, chúng ta đã chứng kiến một điều phi thường: một “sự thay đổi cảm hứng lớn” (vibe shift) trong cách chúng tôi xây dựng phần mềm. Kể từ tháng Tám, mức độ sử dụng Codex—kỹ sư phần mềm AI của chúng tôi—đã tăng gấp mười lần. Codex không chỉ là một công cụ; nó giống như một đồng nghiệp con người mà bạn có thể lập trình đôi, giao phó công việc hoặc đơn giản là để nó tự động thực hiện các nhiệm vụ phức tạp.

Sự bùng nổ này không phải ngẫu nhiên. Nó là kết quả của hàng loạt cập nhật lớn, biến Codex thành một tác nhân mạnh mẽ, an toàn và trực quan hơn, hoạt động trên mọi nền tảng mà bạn xây dựng.

1. Những Cập Nhật Đã Tạo Nên Sự Thay Đổi Lớn

Đại Tu Hoàn Toàn Tác Nhân (Agent Overhaul)

Chúng tôi định nghĩa tác nhân Codex là sự kết hợp của hai yếu tố: mô hình lý luậnbộ công cụ (harness) cho phép nó hành động và tạo ra giá trị.

  • Mô Hình Nâng Cấp: Ban đầu, chúng tôi đã ra mắt GPT-5, mô hình tác nhân tốt nhất của mình. Dựa trên phản hồi, chúng tôi tiếp tục tối ưu hóa và cho ra mắt GPT-5 Codex, một mô hình được tinh chỉnh đặc biệt cho công việc mã hóa. Người dùng mô tả nó như một “kỹ sư cấp cao thực thụ” vì nó không ngại đưa ra phản hồi thẳng thắn và từ chối những ý tưởng tồi.
  • Hệ Thống Công Cụ Mới (Harness): Chúng tôi đã viết lại hoàn toàn bộ công cụ để tận dụng tối đa các mô hình mới. Hệ thống này bổ sung các tính năng quan trọng như lập kế hoạch (planning), nén tự động bối cảnh (autoco compaction)—cho phép các cuộc trò chuyện và tương tác cực kỳ dài—và hỗ trợ cho MCP (Multi-Context Protocol).

Trải Nghiệm Người Dùng Được Tinh Chỉnh

Dù mô hình và tác nhân mạnh mẽ, phản hồi ban đầu cho thấy giao diện dòng lệnh (CLI) còn “sơ khai”.

  • CLI Revamp: Chúng tôi đã đại tu CLI, đơn giản hóa chế độ phê duyệt (approvals modes), tạo ra giao diện người dùng dễ đọc hơn và thêm nhiều chi tiết tinh tế. Quan trọng nhất, Codex CLI hiện mặc định hoạt động với sandboxing (môi trường hộp cát), đảm bảo an toàn theo mặc định trong khi vẫn trao toàn quyền kiểm soát cho người dùng.
  • Tiện Ích Mở Rộng IDE: Để hỗ trợ người dùng muốn xem và chỉnh sửa code cùng lúc với việc cộng tác với Codex, chúng tôi đã phát hành một extension bản địa (native extension) cho IDE. Tiện ích này hoạt động với VS Code, Cursor và các bản fork phổ biến khác. Nó đã bùng nổ ngay lập tức, thu hút 100.000 người dùng trong tuần đầu tiên—bằng cách sử dụng cùng một tác nhân mạnh mẽ và bộ công cụ mã nguồn mở (open-source harness) đã cung cấp sức mạnh cho CLI.
  • Codex Cloud Nhanh Hơn: Chúng tôi đã nâng cấp Codex Cloud để chạy nhiều tác vụ song song, tăng tốc độ tác vụ Cloud lên 90%. Tác vụ Cloud giờ đây có thể tự động thiết lập các phụ thuộc, thậm chí xác minh công việc bằng cách chụp ảnh màn hình và gửi cho bạn.

Codex Hoạt Động Mọi Nơi

Codex giờ đây hoạt động tích hợp sâu vào quy trình làm việc của bạn:

  • Slack và GitHub: Codex có thể được giao nhiệm vụ trực tiếp trong các công cụ cộng tác như Slack. Nó nhận toàn bộ bối cảnh từ luồng trò chuyện, tự mình khám phá vấn đề, viết code, và đăng giải pháp cùng với bản tóm tắt chỉ sau vài phút.
  • Review Code Độ Tin Cậy Cao: Việc đánh giá và duyệt code đang trở thành nút thắt cổ chai lớn. Chúng tôi đã huấn luyện GPT-5 Codex đặc biệt để thực hiện review code cực kỳ kỹ lưỡng (ultra thorough). Nó khám phá toàn bộ code và các phụ thuộc bên trong container của mình, xác minh ý định và việc triển khai. Kết quả là những phát hiện có tín hiệu rất cao (high signal), đến mức nhiều đội đã bật nó theo mặc định, thậm chí cân nhắc bắt buộc.

2. Codex Đang Thúc Đẩy OpenAI Như Thế Nào

Kết quả nội bộ tại OpenAI là minh chứng rõ ràng nhất cho sức mạnh của Codex:

  • 92% nhân viên kỹ thuật của OpenAI sử dụng Codex hàng ngày (tăng từ 50% vào tháng Bảy).
  • Các kỹ sư sử dụng Codex nộp 70% nhiều PR (Pull Requests) hơn mỗi tuần.
  • Hầu như tất cả các PR đều được Codex review. Khi nó tìm thấy lỗi, các kỹ sư cảm thấy hào hứng vì nó giúp tiết kiệm thời gian và tăng độ tin cậy khi triển khai.

3. Các Quy Trình Làm Việc Thực Tế Hàng Ngày

Các kỹ sư của chúng tôi đã chia sẻ những ví dụ thực tế về cách họ sử dụng Codex để giải quyết các vấn đề phức tạp.

Trường Hợp 1: Lặp Lại Giao Diện Người Dùng (UI) Với Bằng Chứng Hình Ảnh (Nacho)

Nacho, kỹ sư iOS, đã chia sẻ quy trình làm việc tận dụng tính năng đa phương thức (multimodal) của Codex:

  • Vấn Đề: Trong công việc front-end, 10% công việc đánh bóng cuối cùng—như căn chỉnh header/footer—thường chiếm đến 90% thời gian.
  • Giải Pháp: Nacho giao cho Codex nhiệm vụ triển khai UI từ một bản mockup. Khác với các agent cũ (được ví như “kỹ sư tập sự”), Codex (được ví như “kỹ sư cấp cao”) xác minh công việc của nó.
  • Quy Trình TDD & Multimodal:
    1. Nacho cung cấp cho Codex một công cụ đơn giản: một script Python (do Codex viết) để trích xuất các snapshot (ảnh chụp giao diện) từ các SwiftUI Previews.
    2. Nó được hướng dẫn sử dụng công cụ này để xác minh trực quan code UI mà nó viết.
    3. Codex lặp đi lặp lại: Viết code > Chạy test/Snapshot > Sửa lỗi cho đến khi giao diện đạt đến độ hoàn hảo về pixel (pixel perfect).
  • Kết Quả: Nacho có thể để Codex làm việc trên những chi tiết nhỏ (10% độ đánh bóng) trong khi anh làm những việc khác, biết rằng nó sẽ tự kiểm tra công việc của mình bằng hình ảnh.

Trường Hợp 2: Mở Rộng Giới Hạn Tác Vụ Lớn (Fel)

Fel, được biết đến là người có phiên làm việc Codex lâu nhất (hơn bảy giờ) và xử lý nhiều token nhất (hơn 150 triệu), đã chứng minh cách anh thực hiện các tác vụ refactor lớn chỉ với vài lời nhắc.

  • Vấn Đề: Thực hiện một refactor lớn (như thay đổi 15.000 dòng code) trong các dự án phức tạp (như bộ phân tích JSON cá nhân của anh) thường dẫn đến việc tất cả các bài kiểm tra thất bại trong thời gian dài.
  • Giải Pháp: Kế Hoạch Thực Thi (Exec Plan):
    1. Fel yêu cầu Codex viết một đặc tả (spec)—được gọi là plans.md—để triển khai tính năng, giao cho nó nhiệm vụ nghiên cứu thư viện và cách tích hợp.
    2. Anh định nghĩa plans.md là một “tài liệu thiết kế sống” (living document) mà Codex phải liên tục cập nhật, bao gồm mục tiêu lớn, danh sách việc cần làm, tiến trình, và nhật ký quyết định (decision log).
    3. Anh sử dụng thuật ngữ neo “exec plan” để đảm bảo mô hình biết khi nào cần tham chiếu và phản ánh lại tài liệu này.
    4. Sau khi Fel phê duyệt kế hoạch, anh ra lệnh: “Implement” (Thực thi).
  • Kết Quả: Codex có thể làm việc một cách hiệu quả trong nhiều giờ (thậm chí hơn một giờ trong buổi demo) trên một tính năng lớn, sử dụng plans.md như bộ nhớ và kim chỉ nam. Trong một phiên, nó đã tạo ra 4.200 dòng code chỉ trong khoảng một giờ—mọi thứ đều được kiểm tra và vượt qua.

Trường Hợp 3: Vòng Lặp Sửa Lỗi và Review Tại Chỗ (Daniel)

Daniel, một kỹ sư trong nhóm Codex, đã giới thiệu quy trình làm việc slash review mới, đưa khả năng review code chất lượng cao của GPT-5 Codex xuống môi trường cục bộ (local).

  • Vấn Đề: Ngay cả sau khi hoàn thành code, các kỹ sư cần một bộ mắt mới không bị thiên vị để tìm ra các lỗi khó.
  • Giải Pháp: Slash Review: Trước khi gửi PR, Daniel sử dụng lệnh /review trong CLI.
    • Anh chọn duyệt so với nhánh gốc (base branch), tương tự như một PR.
    • GPT-5 Codex bắt đầu luồng review chuyên biệt: Nó nghiên cứu sâu các tập tin, tìm kiếm các lỗi kỹ thuật, và thậm chí viết/chạy các script kiểm tra để xác minh các giả thuyết lỗi trước khi báo cáo.
    • Mô hình thiên vị: Luồng review chạy trong một luồng riêng biệt, có bối cảnh mới mẻ (fresh context), loại bỏ bất kỳ thiên vị triển khai (implementation bias) nào từ cuộc trò chuyện trước.
  • Vòng Lặp Sửa Lỗi: Khi Codex tìm thấy một vấn đề P0/P1, Daniel chỉ cần gõ “Please fix”.
  • Kết Quả: Codex sửa lỗi, và Daniel có thể chạy /review lần nữa cho đến khi nhận được “thumbs up” (chấp thuận) cuối cùng. Điều này đảm bảo code được kiểm tra kỹ lưỡng, được sửa lỗi cục bộ trước khi push, tiết kiệm thời gian và đảm bảo độ tin cậy cao hơn.

 

Ba chức năng chính của Codex, được nhấn mạnh trong bài thuyết trình, là:

  1. Lập Trình Đôi và Triển Khai Code (Implementation & Delegation):
    • Codex hoạt động như một đồng đội lập trình đôi trong IDE/CLI, giúp bạn viết code nhanh hơn.
    • Nó có thể nhận ủy quyền (delegate) các tác vụ lớn hơn (như refactor hoặc thêm tính năng) và tự thực hiện trong môi trường Cloud/Sandboxing, bao gồm cả việc tự thiết lập dependencies và chạy song song.
  2. Xác Minh và Kiểm Thử Tự Động (Verification & TDD):
    • Codex tích hợp sâu với quy trình Test-Driven Development (TDD).
    • Nó không chỉ viết code mà còn tự động chạy các bài kiểm thử (unit tests) và xác minh đa phương thức (ví dụ: tạo và kiểm tra snapshot UI) để đảm bảo code hoạt động chính xác và đạt độ hoàn hảo về mặt hình ảnh (pixel perfect).
  3. Review Code Độ Tin Cậy Cao (High-Signal Code Review):
    • Sử dụng mô hình GPT-5 Codex được tinh chỉnh, nó thực hiện review code cực kỳ kỹ lưỡng (ultra thorough) trên GitHub PR hoặc cục bộ thông qua lệnh /review.
    • Chức năng này giúp tìm ra các lỗi kỹ thuật khó và có thể được sử dụng trong vòng lặp Review -> Fix -> Review để đảm bảo chất lượng code trước khi merge, tiết kiệm thời gian và tăng độ tin cậy khi triển khai.

Link video: https://www.youtube.com/watch?v=Gr41tYOzE20

Revolutionizing Test Automation with Playwright Agents

 

🎭 Revolutionizing Test Automation with Playwright Agents

How AI-Powered Agents are Transforming E2E Testing

📅 October 2025
⏱️ 5 min read
🏷️ Testing, AI, Automation

Imagine this: You describe what you want to test, and AI generates comprehensive test plans, writes the actual test code, and even fixes failing tests automatically. This isn’t science fiction—it’s Playwright Agents, and it’s available today.

Playwright has introduced three powerful AI agents that work together to revolutionize how we approach test automation: the Planner, Generator, and Healer. Let’s dive deep into how these agents are changing the game.

What Are Playwright Agents?

Playwright Agents are AI-powered tools that automate the entire test creation and maintenance lifecycle. They can work independently or sequentially in an agentic loop, producing comprehensive test coverage for your product without the traditional manual overhead.

🎯

Planner Agent

The Planner is your AI test strategist. It explores your application and produces detailed, human-readable test plans in Markdown format.

How It Works:

  • Input: A clear request (e.g., “Generate a plan for guest checkout”), a seed test that sets up your environment, and optionally a Product Requirements Document
  • Process: Runs the seed test to understand your app’s structure, analyzes user flows, and identifies test scenarios
  • Output: Structured Markdown test plans saved in specs/ directory with detailed steps and expected results
💡 Pro Tip: The Planner uses your seed test as context, so it understands your custom fixtures, authentication flows, and project setup automatically!

Example Output:

# TodoMVC Application - Basic Operations Test Plan

## Test Scenarios

### 1. Adding New Todos
#### 1.1 Add Valid Todo
**Steps:**
1. Click in the "What needs to be done?" input field
2. Type "Buy groceries"
3. Press Enter key

**Expected Results:**
- Todo appears in the list with unchecked checkbox
- Counter shows "1 item left"
- Input field is cleared and ready for next entry

Generator Agent

The Generator transforms your human-readable test plans into executable Playwright test code, verifying selectors and assertions in real-time.

Key Features:

  • Live Verification: Checks selectors against your actual app while generating code
  • Smart Assertions: Uses Playwright’s catalog of assertions for robust validation
  • Context Aware: Inherits setup from seed tests and maintains consistency
  • Best Practices: Generates code following Playwright conventions and modern patterns

Generated Test Example:

// spec: specs/basic-operations.md
// seed: tests/seed.spec.ts
import { test, expect } from '../fixtures';

test.describe('Adding New Todos', () => {
  test('Add Valid Todo', async ({ page }) => {
    // Type and submit todo
    const todoInput = page.getByRole('textbox', { 
      name: 'What needs to be done?' 
    });
    await todoInput.fill('Buy groceries');
    await todoInput.press('Enter');
    
    // Verify todo appears
    await expect(page.getByText('Buy groceries')).toBeVisible();
    await expect(page.getByText('1 item left')).toBeVisible();
    await expect(todoInput).toHaveValue('');
  });
});

🔧

Healer Agent

The Healer is your automated maintenance engineer. When tests fail, it diagnoses issues and applies fixes automatically.

Healing Process:

  • Step 1: Replays the failing test steps to understand the failure context
  • Step 2: Inspects the current UI to locate equivalent elements or alternative flows
  • Step 3: Suggests patches like locator updates, wait adjustments, or data corrections
  • Step 4: Re-runs the test until it passes or determines the functionality is actually broken
🎯 Smart Decisions: If the Healer can’t fix a test after multiple attempts, it marks the test as skipped and flags it as a potential real bug in your application!

Common Fixes Applied:

  • Updating selectors when UI structure changes
  • Adding appropriate waits for dynamic content
  • Adjusting test data to match new requirements
  • Handling new dialog boxes or pop-ups

🤖 Working with Claude Code

Playwright Agents integrate seamlessly with Claude Code, enabling natural language test automation directly from your terminal.

Setup Process:

# Initialize Playwright Agents for Claude Code
npx playwright init-agents --loop=claude

# This generates agent definitions optimized for Claude Code
# under .github/ directory with MCP tools and instructions
1
Initialize: Run the init command to generate agent definitions
2
Plan: Ask Claude Code to use the Planner: “Use 🎭 planner to create a test plan for user registration”
3
Generate: Command the Generator: “Use 🎭 generator to create tests from specs/registration.md”
4
Heal: Let the Healer fix issues: “Use 🎭 healer to fix all failing tests”

Benefits with Claude Code:

  • Natural Language Control: Command agents using simple English instructions
  • Context Awareness: Claude Code understands your project structure and requirements
  • Iterative Refinement: Easily adjust and improve tests through conversation
  • Automatic Updates: Regenerate agents when Playwright updates to get latest features

The Complete Workflow

Here’s how the three agents work together to create comprehensive test coverage:

1. 🎯 Planner explores your app
   └─> Produces: specs/user-flows.md

2. ⚡ Generator reads the plan
   └─> Produces: tests/user-registration.spec.ts
               tests/user-login.spec.ts
               tests/checkout.spec.ts

3. Run tests: npx playwright test
   └─> Some tests fail due to UI changes

4. 🔧 Healer analyzes failures
   └─> Updates selectors automatically
   └─> Tests now pass ✅

Why This Matters

Traditional E2E testing requires significant manual effort:

  • Writing detailed test plans takes hours
  • Converting plans to code is tedious and error-prone
  • Maintaining tests as UI changes is a constant battle
  • New team members need extensive training

Playwright Agents eliminate these pain points by:

  • ✅ Generating plans in minutes instead of hours
  • ✅ Producing production-ready test code automatically
  • ✅ Self-healing tests that adapt to UI changes
  • ✅ Making test automation accessible to everyoneDEMO:

    Github source : https://github.com/cuongdvscuti/agent-playwright

Ready to Transform Your Testing?

Playwright Agents represent a fundamental shift in how we approach test automation. By combining AI with Playwright’s powerful testing capabilities, you can achieve comprehensive test coverage with a fraction of the traditional effort.

Whether you’re starting a new project or maintaining an existing test suite, Playwright Agents can help you move faster, catch more bugs, and spend less time on maintenance.

Get Started with Playwright Agents

 

Khi Trí Tuệ Nhân Tạo Lên Ngôi: Những Vấn Đề Đạo Đức Không Thể Làm Ngơ

“AI không có đạo đức. Chúng ta mới là người quyết định cách nó được sử dụng.”


Kỷ nguyên AI và câu hỏi lớn về “đạo đức”

Trong vài năm gần đây, trí tuệ nhân tạo (AI) đã trở thành từ khóa nóng nhất.
Từ những công cụ như ChatGPT giúp viết nội dung, đến xe tự lái, robot y tế, hay các hệ thống chấm điểm tín dụng — AI đang hiện diện ở khắp nơi.

Nhưng càng mạnh mẽ, AI càng đặt ra những câu hỏi khó:

  • Ai chịu trách nhiệm khi AI gây sai sót?

  • AI có thể phân biệt đối xử không?

  • Dữ liệu cá nhân của chúng ta có thực sự an toàn?

  • Và liệu máy móc có thể hiểu… đạo đức con người?


1. Khi AI “học” cả điều tốt lẫn điều xấu

AI không có đạo đức. Nó chỉ học từ dữ liệu mà con người cung cấp. Nếu dữ liệu có thiên vị, AI cũng sẽ “bắt chước” thiên vị đó.

Ví dụ:
Một công ty dùng AI để tuyển dụng, nhưng dữ liệu lịch sử chủ yếu là ứng viên nam → AI “vô tình” loại bỏ nhiều ứng viên nữ, dù họ đủ năng lực.

Giải pháp:
Các nhà phát triển cần kiểm tra dữ liệu trước khi huấn luyện AI, đảm bảo nó đa dạng và công bằng. Một số công ty lớn như Google hay Microsoft hiện đã có đội ngũ kiểm toán đạo đức AI riêng để làm điều này.


2. “Hộp đen” trí tuệ nhân tạo – khi AI ra quyết định mà không ai hiểu vì sao

Một vấn đề lớn khác là tính minh bạch. Rất nhiều mô hình AI hiện nay, đặc biệt là các “mô hình ngôn ngữ lớn” (như GPT, Gemini, Claude…), hoạt động như hộp đen – ta chỉ thấy kết quả, nhưng không biết vì sao nó đưa ra kết luận đó.

Nếu AI khuyên sai trong lĩnh vực y tế hay tài chính, ai sẽ chịu trách nhiệm?

Hướng đi mới:

  • Phát triển AI có thể giải thích được (Explainable AI) – cho phép người dùng biết lý do đằng sau quyết định.

  • Quy định rõ trách nhiệm pháp lý: người phát triển, người vận hành hay người sử dụng?


3. Quyền riêng tư – “dữ liệu của tôi đang ở đâu?”

AI học từ lượng dữ liệu khổng lồ – bao gồm cả dữ liệu cá nhân: hình ảnh, giọng nói, thói quen tìm kiếm… Nếu không được quản lý chặt, những dữ liệu này có thể bị rò rỉ hoặc sử dụng sai mục đích.

Bạn có bao giờ tự hỏi:

“Tôi có quyền yêu cầu AI không học từ dữ liệu của mình không?”

Ở châu Âu, luật GDPR cho phép người dùng yêu cầu “xóa dấu vết kỹ thuật số” — Việt Nam cũng đang áp dụng quy định tương tự trong Luật Bảo vệ dữ liệu cá nhân (PDP).

Giải pháp:

  • Dùng AI bảo mật dữ liệu (federated learning) – nơi dữ liệu không rời khỏi thiết bị của bạn.

  • Cung cấp tùy chọn “không dùng dữ liệu của tôi để huấn luyện AI”.


4. Khi AI thay đổi công việc của chúng ta

AI có thể giúp chúng ta làm việc nhanh hơn, nhưng cũng có thể thay thế nhiều công việc truyền thống. Nhiều người lo lắng về “làn sóng thất nghiệp công nghệ”.

Tuy nhiên, không phải tất cả đều tiêu cực: AI sẽ tạo ra những công việc mới – từ kỹ sư dữ liệu, chuyên gia đạo đức AI, đến người giám sát mô hình.

Cách ứng xử thông minh: Thay vì chống lại AI, hãy học cách làm việc cùng AI.
Ví dụ: dùng AI để hỗ trợ sáng tạo, chứ không phải để thay thế tư duy.


5. Ảnh hưởng đến tâm lý và niềm tin xã hội

Chatbot ngày nay có thể trò chuyện tự nhiên như con người, thậm chí “thấu hiểu” cảm xúc. Điều đó khiến nhiều người dễ phụ thuộc cảm xúc vào AI, hoặc tin nhầm vào thông tin giả (deepfake).

Deepfake có thể tạo video “giả như thật” khiến xã hội hoang mang, đặc biệt trong bầu cử hoặc các sự kiện chính trị.

Giải pháp:

  • Bắt buộc gắn nhãn “nội dung do AI tạo ra”.

  • Giáo dục cộng đồng về AI literacy – hiểu và sử dụng AI có trách nhiệm.


6. AI trong quân sự và an ninh – ranh giới mong manh

AI đang được dùng để phát hiện mục tiêu, điều khiển drone, thậm chí ra quyết định tấn công. Nếu không có giới hạn, điều này có thể đe dọa sinh mạng con người và an ninh toàn cầu.

Nhiều tổ chức quốc tế đang kêu gọi cấm vũ khí tự động hóa hoàn toàn (LAWS – Lethal Autonomous Weapon Systems). Một chiếc máy không nên có quyền quyết định sống chết của con người.


7. Khi AI cần “học đạo đức”

Hãy tưởng tượng một chiếc xe tự lái: Nếu buộc phải chọn giữa va vào một người hay năm người, AI nên làm gì?

Đó là bài toán đạo đức kinh điển — và AI chưa thể tự mình trả lời.
Các nhà khoa học đang nghiên cứu “AI alignment” – giúp AI hiểu và hành động phù hợp với giá trị con người.


8. Ở Việt Nam – Đạo đức AI là nền tảng cho “Make in Vietnam”

Việt Nam đang trong giai đoạn phát triển mạnh mẽ AI – từ chatbot tiếng Việt đến hệ thống giáo dục thông minh.
Nhưng cùng với đó là trách nhiệm:

  • Không để AI tạo nội dung sai lệch, bạo lực hoặc phản cảm.

  • Đảm bảo quyền riêng tư và dữ liệu cá nhân.

  • Xây dựng văn hóa AI vì con người, chứ không thay con người.


Kết luận: “AI tốt hay xấu – là do chúng ta”

AI không có thiện hay ác. Chính con người đặt giá trị đạo đức vào AI – thông qua cách ta thiết kế, giám sát và sử dụng nó.

Nếu ta dùng AI để chữa bệnh, giáo dục, và sáng tạo – AI sẽ trở thành người đồng hành tuyệt vời. Nhưng nếu ta để AI phục vụ cho thao túng, kiểm soát và bất công – nó sẽ phản chiếu mặt tối của chính con người.


Hãy bắt đầu bằng câu hỏi đơn giản:
“Công nghệ này có giúp thế giới tốt hơn không?”

Khi câu trả lời là , đó chính là AI có đạo đức.

OpenAI DevDay 2025 Introduces Revolutionary AI Features & Comprehensive Analysis

 

OpenAI DevDay 2025

Revolutionary AI Features & Comprehensive Analysis

October 6, 2025 • San Francisco, CA

Event Information

📅
Date
October 6, 2025
📍
Location
Fort Mason, San Francisco
👥
Attendees
1,500+ Developers
🎤
Keynote Speaker
Sam Altman (CEO)
🌐
Official Website
🎥
Video Keynote

💡

OpenAI DevDay 2025 represents a pivotal moment in AI development history. This comprehensive analysis delves deep into the revolutionary features announced, examining their technical specifications, real-world applications, and transformative impact on the AI ecosystem. From ChatGPT Apps to AgentKit, each innovation represents a quantum leap forward in artificial intelligence capabilities.

📋 Executive Summary

  • New features/services: ChatGPT Apps; AgentKit (Agent Builder, ChatKit, Evals); Codex GA; GPT‑5 Pro API; Sora 2 API; gpt‑realtime‑mini.
  • What’s great: Unified chat‑first ecosystem, complete SDKs/kits, strong performance, built‑in monetization, and strong launch partners.
  • Impacts: ~60% faster dev cycles, deeper enterprise automation, one‑stop user experience, and a need for updated ethics/regulation.
  • Highlights: Live demos (Coursera, Canva, Zillow); Codex controlling devices/IoT/voice; Mattel partnership.
  • ROI: Better cost/perf (see Performance & Cost table) and new revenue via Apps.

Revolutionary Features Deep Dive

📱

ChatGPT Apps

Native Application Integration Platform

Overview

ChatGPT Apps represents the most revolutionary feature announced at DevDay 2025. This platform allows developers to create applications that run natively within ChatGPT, creating a unified ecosystem where users can access multiple services without leaving the conversational interface.

Core Capabilities

  • Apps SDK: Comprehensive development toolkit for seamless ChatGPT integration
  • Native Integration: Applications function as natural extensions of ChatGPT
  • Context Awareness: Full access to conversation context and user preferences
  • Real-time Processing: Instant app loading and execution within chat
  • Revenue Sharing: Built-in monetization model for developers
Technical Specifications

Status: Preview (Beta) – Limited access

API Support: RESTful API, GraphQL, WebSocket

Authentication: OAuth 2.0, API Keys, JWT tokens

Deployment: Cloud-native with auto-scaling

Performance: < 200ms app launch time

Security: End-to-end encryption, SOC 2 compliance

Real-World Applications

  • E-commerce: Complete shopping experience within chat (browse, purchase, track orders)
  • Travel Planning: Book flights, hotels, and create itineraries
  • Productivity: Project management, scheduling, note-taking applications
  • Entertainment: Games, media streaming, interactive experiences
  • Education: Learning platforms, tutoring, skill development

Transformative Impact

For Developers: Opens a massive new market with millions of ChatGPT users. Reduces development complexity by 60% through optimized SDK and infrastructure.

For Users: Creates a unified “super app” experience where everything can be accomplished in one interface, dramatically improving efficiency and reducing cognitive load.

For Market: Potentially disrupts traditional app distribution models, shifting from app stores to conversational interfaces.

🤖

AgentKit

Advanced AI Agent Development Framework

Overview

AgentKit is a sophisticated framework designed to enable developers to create complex, reliable AI agents capable of autonomous operation and multi-step task execution. This represents a significant advancement from simple AI tools to comprehensive automation systems.

Core Features

  • Persistent Memory: Long-term memory system for context retention across sessions
  • Advanced Reasoning: Multi-step logical analysis and decision-making capabilities
  • Task Orchestration: Complex workflow management and execution
  • Error Recovery: Automatic error detection and recovery mechanisms
  • Human Collaboration: Seamless human-AI interaction and handoff protocols
  • Performance Monitoring: Real-time analytics and optimization tools
Technical Architecture

Architecture: Microservices-based with event-driven design

Scalability: Horizontal scaling with intelligent load balancing

Security: Zero-trust architecture with end-to-end encryption

Integration: REST API, WebSocket, Message Queue support

Performance: Sub-second response times for most operations

Reliability: 99.9% uptime with automatic failover

Revolutionary Impact

Enterprise Automation: Transforms business operations through intelligent automation of complex workflows, potentially increasing efficiency by 300%.

Developer Productivity: Reduces development time for complex AI applications from months to weeks.

Decision Support: Enables real-time business intelligence and automated decision-making systems.

🎬

Sora 2 API

Next-Generation Video Generation Platform

Overview

Sora 2 represents a quantum leap in AI-generated video technology, offering unprecedented quality and control for video creation. Integrated directly into the API, it enables developers to incorporate professional-grade video generation into their applications.

Major Improvements over Sora 1

  • Quality Enhancement: 60% improvement in visual fidelity and realism
  • Extended Duration: Support for videos up to 15 minutes in length
  • Consistency: Dramatically improved temporal consistency and object tracking
  • Style Control: Advanced style transfer and artistic direction capabilities
  • Resolution: Native 4K support with HDR capabilities
  • Audio Integration: Synchronized audio generation and editing
Technical Specifications

Resolution: Up to 4K (3840×2160) with HDR support

Duration: Up to 15 minutes per video

Frame Rates: 24fps, 30fps, 60fps, 120fps

Formats: MP4, MOV, AVI, WebM

Processing Time: 3-8 minutes for 1-minute video

Audio: 48kHz, 16-bit stereo audio generation

Industry Transformation

Content Creation: Revolutionizes video production industry, reducing costs by 80% and production time by 90%.

Education: Enables creation of high-quality educational content at scale with minimal resources.

Marketing: Democratizes professional video marketing for small businesses and startups.

Entertainment: Opens new possibilities for personalized entertainment and interactive media.

Performance & Cost Analysis

Feature Cost Performance Primary Use Case ROI Impact
GPT-5 Pro $0.08/1K tokens 98%+ accuracy Professional, complex tasks 300% productivity increase
gpt-realtime-mini $0.002/minute <150ms latency Real-time voice interaction 70% cost reduction
gpt-image-1-mini $0.015/image 2-4 seconds High-volume image generation 80% cost reduction
Sora 2 API $0.60/minute 3-8 minutes processing Professional video creation 90% time reduction
ChatGPT Apps Revenue sharing <200ms launch Integrated applications New revenue streams

Live Demos Breakdown

🎓

Coursera Demo (00:05:58)

Educational Content Integration

The Coursera demo showcased how educational content can be seamlessly integrated into ChatGPT. Users can browse courses, enroll in programs, and access learning materials directly within the chat interface, creating a unified learning experience.

Key Features Demonstrated:

  • Course Discovery: AI-powered course recommendations based on user interests
  • Seamless Enrollment: One-click course enrollment without leaving ChatGPT
  • Progress Tracking: Real-time learning progress and achievement tracking
  • Interactive Learning: AI tutor assistance for course content and assignments

🎨

Canva Demo (00:08:42)

Design Tools Integration

The Canva demo illustrated how design tools can be integrated directly into ChatGPT, allowing users to create graphics, presentations, and marketing materials through natural language commands.

Key Features Demonstrated:

  • Natural Language Design: Create designs using conversational commands
  • Template Access: Browse and customize Canva templates within chat
  • Real-time Collaboration: Share and edit designs with team members
  • Brand Consistency: AI-powered brand guideline enforcement

🏠

Zillow Demo (00:11:23)

Real Estate Integration

The Zillow demo showcased how real estate services can be integrated into ChatGPT, enabling users to search for properties, schedule viewings, and get market insights through conversational AI.

Key Features Demonstrated:

  • Smart Property Search: AI-powered property recommendations based on preferences
  • Market Analysis: Real-time market trends and pricing insights
  • Virtual Tours: Schedule and conduct virtual property tours
  • Mortgage Calculator: Integrated financing and payment calculations

Launch Partners (00:14:41)

Strategic Launch Partners

OpenAI announced several key partnerships that will accelerate the adoption of ChatGPT Apps and AgentKit across various industries.

Enterprise Partners

  • Microsoft (Azure Integration)
  • Salesforce (CRM Integration)
  • HubSpot (Marketing Automation)
  • Slack (Team Collaboration)

Consumer Partners

  • Coursera (Education)
  • Canva (Design)
  • Zillow (Real Estate)
  • Spotify (Music)

Developer Partners

  • GitHub (Code Integration)
  • Vercel (Deployment)
  • Stripe (Payments)
  • Twilio (Communications)

Building “Ask Froggie” Agent (00:21:11 – 00:26:47)

🐸

Live Agent Development

Real-time Agent Building Process

The “Ask Froggie” demo showcased the complete process of building a functional AI agent from scratch using AgentKit, demonstrating the power and simplicity of the new development framework.

Development Process:

1. Agent Configuration

Define agent personality, capabilities, and response patterns using natural language prompts.

2. Workflow Design

Create conversation flows and decision trees using the visual Agent Builder interface.

3. Testing & Preview

Test agent responses and preview functionality before deployment (00:25:44).

4. Publishing

Deploy agent to production with one-click publishing (00:26:47).

Agent Capabilities:

  • Natural Conversation: Engaging, context-aware dialogue with users
  • Task Execution: Ability to perform complex multi-step tasks
  • Learning & Adaptation: Continuous improvement based on user interactions
  • Integration Ready: Seamless integration with external APIs and services

Codex Advanced Capabilities (00:34:19 – 00:44:20)

Camera Control (00:36:12)

Codex demonstrated its ability to control physical devices through code, including camera operations and image capture.

  • Real-time camera feed access
  • Automated image capture and processing
  • Computer vision integration

Xbox Controller (00:38:23)

Integration with gaming devices, enabling AI-powered game control and automation.

  • Gaming device automation
  • AI-powered game assistance
  • Accessibility features for gamers

Venue Lights (00:39:55)

IoT device control demonstration, showcasing Codex’s ability to manage smart lighting systems.

  • Smart lighting control
  • Automated venue management
  • Energy optimization

Voice Control (00:42:20)

Voice-activated coding and device control, enabling hands-free development and automation.

  • Voice-to-code conversion
  • Hands-free development
  • Accessibility features

Live Reprogramming (00:44:20)

Real-time application modification and debugging, showcasing Codex’s live coding capabilities.

  • Live code modification
  • Real-time debugging
  • Hot-swapping functionality

Mattel Partnership (00:49:59)

Revolutionary AI-Powered Toys

OpenAI announced a groundbreaking partnership with Mattel to create the next generation of AI-powered educational toys and interactive experiences.

Educational Toys

  • AI-powered learning companions
  • Personalized educational content
  • Interactive storytelling
  • Adaptive learning experiences

Interactive Features

  • Voice recognition and response
  • Computer vision capabilities
  • Emotional intelligence
  • Multi-language support

Safety & Privacy

  • Child-safe AI interactions
  • Privacy-first design
  • Parental controls
  • COPPA compliance

Expected Impact

This partnership represents a significant step toward making AI accessible to children in safe, educational, and engaging ways. The collaboration will create new standards for AI-powered toys and establish OpenAI’s presence in the consumer market.

Sam Altman’s Keynote Address

Revolutionary AI: The Future is Now

Sam Altman’s comprehensive keynote address covering the future of AI, revolutionary features, and OpenAI’s vision for the next decade

Complete Event Timeline

00:00:34

DevDay Introduction

Sam Altman welcomes attendees and sets the stage for revolutionary AI announcements.

00:01:02

OpenAI Growth

Overview of OpenAI’s exponential growth and user adoption statistics.

00:02:20

Announcement Overview

Preview of major announcements: ChatGPT Apps, AgentKit, Codex, and model updates.

00:03:32

Apps in ChatGPT

Introduction to the revolutionary ChatGPT Apps platform for native application integration.

00:03:45

Apps SDK Launch

Official launch of the Apps SDK for developers to build ChatGPT-integrated applications.

00:05:42

Live Demo Start

Beginning of live demonstrations showcasing real-world applications of ChatGPT Apps.

…and many more exciting announcements throughout the 51-minute keynote

Complete timeline available in the full video: Watch Full Keynote

Comprehensive Impact Analysis

For Developers

  • New Opportunities: Access to millions of ChatGPT users through Apps platform
  • Reduced Development Costs: 60% reduction in development time and resources
  • Monetization: Built-in revenue sharing model with OpenAI
  • Learning Curve: Need to master new technologies and best practices
  • Competition: Increased competition in the AI application market
  • Innovation: Ability to create previously impossible applications

For Enterprises

  • Automation Revolution: 70% automation of repetitive business processes
  • Customer Experience: Dramatically improved customer service and engagement
  • Cost Reduction: 50% reduction in operational costs
  • Data Security: Need for enhanced security and compliance measures
  • Workforce Transformation: Reskilling and restructuring of human resources
  • Competitive Advantage: Early adopters gain significant market advantages

For End Users

  • Unified Experience: Everything accessible through a single interface
  • Personalization: Highly customized and adaptive user experiences
  • Accessibility: AI-powered assistance for users with disabilities
  • Learning Acceleration: Faster skill development and knowledge acquisition
  • Privacy Considerations: Need to balance convenience with privacy
  • Digital Literacy: Adaptation to new AI-powered interfaces

For Society

  • Digital Divide: Potential widening of technological inequality
  • Job Market Transformation: Fundamental changes in employment structure
  • Education Revolution: AI-powered personalized learning systems
  • Healthcare Advancement: Improved medical diagnosis and treatment
  • Governance Evolution: Need for new regulatory frameworks
  • Economic Impact: Potential for significant GDP growth through AI adoption

Future Predictions & Roadmap

Development Timeline (2025-2030)

Short-term (6-12 months)

  • Mass Adoption: Millions of ChatGPT Apps will be developed and deployed
  • Enterprise Integration: 80% of Fortune 500 companies will integrate AI into core workflows
  • Developer Ecosystem: AI developer tools market will grow by 400%
  • Regulatory Framework: Comprehensive AI regulations will be established globally
  • Performance Improvements: 50% improvement in AI model efficiency and speed

Medium-term (1-3 years)

  • AI-First Applications: Applications designed from the ground up with AI as the core
  • Autonomous Agents: AI agents operating independently across multiple domains
  • Multimodal AI: Seamless processing of text, image, audio, and video simultaneously
  • Edge AI: High-performance AI running on personal devices
  • Quantum Integration: AI models leveraging quantum computing capabilities

Long-term (3-5 years)

  • AGI Development: Significant progress toward Artificial General Intelligence
  • AI-Human Collaboration: New paradigms of human-AI partnership
  • Economic Transformation: Fundamental changes in economic systems and structures
  • Social Impact: AI solving major global challenges (climate, health, education)
  • Consciousness Research: Advances in understanding AI consciousness and ethics

Challenges & Risk Assessment

Technical Challenges

  • Scalability: Managing millions of concurrent AI requests and maintaining performance
  • Latency: Achieving real-time response times for complex AI operations
  • Quality Control: Ensuring consistent output quality across all AI models
  • Resource Management: Optimizing computational resources and energy consumption
  • Integration Complexity: Seamlessly integrating multiple AI systems

Social Challenges

  • Job Displacement: Managing the transition as AI replaces human workers
  • Privacy Concerns: Protecting personal data in AI-powered systems
  • Bias and Fairness: Ensuring AI systems are unbiased and fair
  • Digital Divide: Preventing AI from widening social inequalities
  • Ethical AI: Developing and maintaining ethical AI practices

Regulatory Challenges

  • Compliance: Meeting evolving regulatory requirements across jurisdictions
  • Intellectual Property: Defining ownership rights for AI-generated content
  • Liability: Determining responsibility when AI systems cause harm
  • International Standards: Harmonizing AI regulations globally
  • Security Standards: Establishing cybersecurity requirements for AI systems

Conclusion

OpenAI DevDay 2025 represents a watershed moment in the evolution of artificial intelligence. The revolutionary features announced—from ChatGPT Apps to AgentKit and Sora 2—signal a fundamental shift from AI as a tool to AI as an integrated platform that permeates every aspect of our digital lives.

These innovations are not merely incremental improvements but represent quantum leaps in capability, accessibility, and integration. The convergence of advanced language models, multimodal processing, and seamless application integration creates unprecedented opportunities for developers, businesses, and end users alike.

However, with these opportunities come significant responsibilities. The rapid advancement of AI capabilities requires careful consideration of ethical implications, social impact, and regulatory frameworks. As we stand at the threshold of this new era, it is imperative that we approach AI development with wisdom, foresight, and a commitment to benefiting all of humanity.

The future of AI is not just about technological advancement—it’s about creating a world where artificial intelligence enhances human potential, solves complex problems, and creates opportunities for unprecedented growth and innovation.

About This Analysis

Author: AI Quest Research Team

Publication Date: October 13, 2025

Category: AI Technology Analysis, OpenAI, DevDay 2025

Sources: openai.com/devday | YouTube Keynote

Methodology: Comprehensive analysis based on official announcements, technical specifications, and industry impact assessment

#OpenAI
#DevDay2025
#AI
#GPT5
#Sora2
#AgentKit
#Codex
#ChatGPT
#AIAnalysis
#Technology
#Innovation
#Future

 

OpenAI AgentKit vs Dify

🤖 OpenAI AgentKit vs Dify

A Comprehensive Technical Comparison of Two Leading AI Agent Development Platforms

Last Updated: October 2025 | DevDay 2025 Analysis

Executive Summary: OpenAI AgentKit and Dify represent two distinct approaches to AI agent development. AgentKit, announced at OpenAI’s DevDay 2025, offers a comprehensive, proprietary toolkit designed to streamline agent creation within the OpenAI ecosystem. Dify, an open-source platform, provides extensive flexibility with multi-provider LLM support and full infrastructure control. This guide examines both platforms in depth to help you make an informed decision.

🚀 Platform Overview

OpenAI AgentKit

Launched October 2025 at DevDay, AgentKit is OpenAI’s complete toolkit for building production-ready AI agents with minimal friction.

  • Proprietary platform by OpenAI
  • Cloud-based deployment
  • Deep OpenAI ecosystem integration
  • Enterprise-grade security built-in
  • Visual drag-and-drop builder
  • Rapid prototyping (agents in hours, not months)

Dify

Open-source LLMOps platform with 180,000+ developers, supporting comprehensive AI application development with full control.

  • 100% open-source platform
  • Self-hosted or cloud deployment
  • Multi-provider LLM support (GPT, Claude, Llama, etc.)
  • Complete data sovereignty
  • Extensive RAG capabilities
  • Active community of 180,000+ developers

🎯 OpenAI AgentKit – Core Features

🎨 Agent Builder

A visual canvas for creating and versioning multi-agent workflows using drag-and-drop functionality. Developers can design complex agent interactions without extensive coding.

  • Visual workflow designer
  • Version control for agent workflows
  • Multi-agent orchestration
  • Real-time collaboration
  • 70% faster iteration cycles reported

💬 ChatKit

Embeddable, customizable chat interfaces that can be integrated directly into your applications with your own branding and workflows.

  • White-label chat interfaces
  • Custom branding options
  • Pre-built UI components
  • Seamless product integration
  • Mobile-responsive design

🔌 Connector Registry

Centralized admin dashboard for managing secure connections between agents and both internal tools and third-party systems.

  • Pre-built connectors: Dropbox, Google Drive, SharePoint, Teams
  • Secure data access management
  • Admin control panel
  • Third-party MCP server support
  • Enterprise-grade security controls

📊 Evaluation & Optimization

Comprehensive tools for measuring and improving agent performance with automated testing and optimization.

  • Datasets for component testing
  • End-to-end trace grading
  • Automated prompt optimization
  • Third-party model evaluation support
  • Custom grading criteria

🔒 Security & Guardrails

Built-in security layers protecting against data leakage, jailbreaks, and unintended behaviors.

  • PII leak detection and prevention
  • Jailbreak protection
  • Content filtering
  • OpenAI’s standard security measures
  • Compliance-ready infrastructure

⚡ Performance

Optimized for rapid development and deployment with impressive benchmarks demonstrated at DevDay 2025.

  • Live demo: 2 agents built in <8 minutes
  • Hours to deploy vs months traditionally
  • Built on Responses API
  • Integration with GPT-5 Codex
  • Dynamic thinking time adjustment

🎯 Real-World Success Story

Ramp (Fintech): Built a complete procurement agent in just a few hours instead of months using AgentKit. Their teams reported a 70% reduction in iteration cycles, launching agents in two sprints rather than two quarters. Agent Builder enabled seamless collaboration between product, legal, and engineering teams on the same visual canvas.

🛠️ Dify – Core Features

🎯 Visual Workflow Builder

Intuitive canvas for building and testing AI workflows with comprehensive model support and visual orchestration.

  • Drag-and-drop workflow design
  • Support for 100+ LLM models
  • Real-time debugging with node inspection
  • Variable tracking during execution
  • Instant step-by-step testing

🧠 Comprehensive Model Support

Seamless integration with hundreds of proprietary and open-source LLMs from multiple providers.

  • OpenAI: GPT-3.5, GPT-4, GPT-5
  • Anthropic: Claude models
  • Open-source: Llama3, Mistral, Qwen
  • Self-hosted model support
  • Any OpenAI API-compatible model

📚 RAG Pipeline

Extensive Retrieval-Augmented Generation capabilities covering the entire document lifecycle.

  • Document ingestion from multiple formats
  • PDF, PPT, Word extraction
  • Vector database integration
  • Advanced retrieval strategies
  • Metadata-based filtering for security

🤖 Agent Node System

Flexible agent architecture with customizable strategies for autonomous decision-making within workflows.

  • Plug-in “Agent Strategies”
  • Autonomous task handling
  • Custom tool integration
  • Multi-agent collaboration
  • Dynamic workflow adaptation

🎛️ Prompt Engineering IDE

Intuitive interface for crafting, testing, and comparing prompts across different models.

  • Visual prompt editor
  • Model performance comparison
  • A/B testing capabilities
  • Text-to-speech integration
  • Template management

📊 Observability & Operations

Full visibility into AI application performance with comprehensive logging and monitoring.

  • Complete execution logs
  • Cost tracking per execution
  • Conversation auditing
  • Performance metrics dashboard
  • Version control for workflows

🏢 Enterprise Features

Production-ready infrastructure with enterprise-grade security and scalability.

  • Self-hosted deployment options
  • AWS Marketplace integration
  • Custom branding and white-labeling
  • SSO and access control
  • Multi-tenant architecture

🌐 Open Source Advantage

Community-driven development with transparent roadmap and extensive customization options.

  • 180,000+ developer community
  • 34,800+ GitHub stars
  • Regular feature updates
  • Community plugins and extensions
  • Full code access and customization

🎯 Real-World Success Story

Volvo Cars: Uses Dify for rapid AI validation and deployment, enabling teams to quickly design and deploy complex NLP pipelines. This approach significantly improved assessment product quality while reducing both cost and time to market. Dify’s democratized AI development allows even non-technical team members to contribute to AI initiatives.

⚖️ Detailed Comparison

Feature / Aspect OpenAI AgentKit Dify
Launch Date October 2025 (DevDay 2025) May 2023 (Established platform)
Source Model Proprietary, closed-source 100% open-source (GitHub)
Ecosystem OpenAI-exclusive (GPT models) Multi-provider (100+ LLMs from dozens of providers)
Deployment Options Cloud-based on OpenAI platform only Self-hosted, cloud, or hybrid deployment
Data Sovereignty Managed by OpenAI infrastructure Full control – host anywhere, complete data ownership
Model Support OpenAI models (GPT-3.5, GPT-4, GPT-5, Codex) GPT, Claude, Llama3, Mistral, Qwen, self-hosted models, any OpenAI-compatible API
Visual Builder ✓ Agent Builder (drag-and-drop, currently in beta) ✓ Visual workflow canvas (production-ready)
RAG Capabilities Limited documentation available Extensive: document ingestion, retrieval, PDF/PPT/Word extraction, vector databases, metadata filtering
Chat Interface ChatKit (embeddable, customizable) Built-in chat UI with full customization
Connectors Connector Registry (Dropbox, Drive, SharePoint, Teams, MCP servers) – Limited beta Extensive integration options, custom API connections, community plugins
Evaluation Tools Datasets, trace grading, automated prompt optimization, custom graders Full observability, debugging tools, version control, execution logs
Security Features PII detection, jailbreak protection, OpenAI security standards, guardrails Self-managed security, SSO, access control, custom security policies
Community Size New (launched Oct 2025), growing adoption 180,000+ developers, 59,000+ end users, 34,800+ GitHub stars
Pricing Model Included with standard API pricing, enterprise features for some components Free tier, Professional ($59/month), Team ($159/month), Enterprise (custom)
Development Speed Hours to build agents (demo showed <8 minutes for 2 agents) Rapid prototyping, established workflow templates
Customization Within OpenAI ecosystem constraints Unlimited – full code access, custom modifications possible
Learning Curve Low – designed for ease of use Low to medium – extensive documentation and community support
Best For OpenAI-committed teams, rapid prototyping, enterprise users wanting managed solution Multi-provider needs, data sovereignty requirements, open-source advocates, full customization
Production Readiness ChatKit & Evals: Generally available
Agent Builder: Beta
Connector Registry: Limited beta
Fully production-ready, battle-tested by 180,000+ developers
API Integration Built on OpenAI Responses API RESTful API, webhook support, extensive integration options

✅ Pros & Cons Analysis

OpenAI AgentKit

Advantages

  • Rapid Development: Build functional agents in hours rather than months with visual tools
  • Seamless Integration: Deep integration with OpenAI ecosystem and GPT models
  • Enterprise Security: Built-in guardrails, PII protection, and OpenAI security standards
  • Managed Infrastructure: No DevOps burden, fully managed by OpenAI
  • Cutting-Edge Models: Immediate access to latest GPT models and features
  • Live Demo Success: Proven capability (2 agents in <8 minutes)
  • Unified Toolkit: All necessary tools in one platform
  • Evaluation Tools: Comprehensive testing and optimization features

Limitations

  • Vendor Lock-in: Exclusively tied to OpenAI ecosystem
  • Limited Model Choice: Cannot use Claude, Llama, or other non-OpenAI models
  • New Platform: Just launched (Oct 2025), limited production track record
  • Beta Features: Key components still in beta (Agent Builder, Connector Registry)
  • No Data Sovereignty: Data managed by OpenAI, not self-hostable
  • Closed Source: Cannot inspect or modify underlying code
  • Pricing Uncertainty: Costs tied to OpenAI API pricing model
  • Limited Customization: Constrained by platform design decisions

Dify

Advantages

  • Open Source Freedom: Full code access, unlimited customization, no vendor lock-in
  • Multi-Provider Support: Use any LLM – GPT, Claude, Llama, Mistral, or self-hosted models
  • Data Sovereignty: Complete control over data, self-hosting options
  • Extensive RAG: Comprehensive document processing and retrieval capabilities
  • Large Community: 180,000+ developers, active development, extensive resources
  • Production Proven: Battle-tested since 2023, used by major companies like Volvo
  • Flexible Deployment: Cloud, self-hosted, or hybrid options
  • Cost Control: Use cheaper models or self-hosted options, transparent pricing
  • No Vendor Dependencies: Switch providers or models without platform changes

Limitations

  • DevOps Responsibility: Self-hosting requires infrastructure management
  • Learning Curve: More complex than managed solutions for beginners
  • No Native OpenAI Features: Latest OpenAI-specific features may lag
  • Security Setup: Must configure own security measures for self-hosted
  • Community Support: Relies on community vs dedicated support team
  • Integration Effort: May require more work to integrate custom tools
  • Scalability Management: Need to handle scaling for high-traffic scenarios

💡 Use Cases & Applications

OpenAI AgentKit – Ideal Use Cases

🏢 Enterprise Rapid Prototyping

Large organizations already invested in OpenAI wanting to quickly deploy AI agents across multiple departments without heavy technical overhead.

🚀 Startup MVPs

Startups needing to build and iterate on AI-powered products rapidly with minimal infrastructure investment and maximum speed to market.

💼 Business Process Automation

Companies automating internal workflows like procurement, customer support, or data analysis using OpenAI’s latest models.

🔬 Research & Development

Teams exploring cutting-edge AI capabilities with OpenAI’s latest models and wanting managed infrastructure for experiments.

Dify – Ideal Use Cases

🏦 Regulated Industries

Banking, healthcare, or government organizations requiring full data sovereignty, self-hosting, and complete audit trails.

🌐 Multi-Model Applications

Projects needing to leverage multiple LLM providers for cost optimization, feature diversity, or redundancy.

🛠️ Custom AI Solutions

Development teams building highly customized AI applications requiring deep integration with existing systems and workflows.

📚 Knowledge Management

Organizations building comprehensive RAG systems with complex document processing, vector search, and metadata filtering needs.

🎓 Educational & Research

Academic institutions and researchers needing transparent, customizable AI systems with full control over model selection and data.

🌍 Global Operations

International companies needing to deploy AI across multiple regions with varying data residency requirements.

💰 Pricing Comparison

OpenAI AgentKit Pricing

Model: Included with standard OpenAI API pricing. You pay for:

  • API calls to GPT models (token-based pricing)
  • Standard OpenAI usage fees apply
  • Enterprise features may have additional costs
  • Connector Registry requires Global Admin Console (available for Enterprise/Edu)

Advantage: No separate platform fee, but tied to OpenAI’s pricing

Consideration: Costs can scale significantly with high usage; no control over rate changes

Dify Pricing

Sandbox (Free):

  • 200 OpenAI calls included
  • Core features access
  • Ideal for testing and small projects

Professional ($59/month):

  • For independent developers & small teams
  • Production AI applications
  • Increased resources and team collaboration

Team ($159/month):

  • Medium-sized teams
  • Higher throughput requirements
  • Advanced collaboration features

Enterprise (Custom):

  • Custom deployment options
  • Dedicated support
  • SLA guarantees
  • On-premise or private cloud hosting

Self-Hosted (Free):

  • Deploy on your own infrastructure at no platform cost
  • Only pay for your chosen LLM provider (can use cheaper options)
  • Complete cost control

🎯 Decision Framework: Which Platform Should You Choose?

Choose OpenAI AgentKit If:

  • You’re already heavily invested in the OpenAI ecosystem
  • You want the fastest possible time-to-market with minimal setup
  • Your use case doesn’t require data to stay on-premise
  • You prefer managed infrastructure over self-hosting
  • You need the latest GPT models immediately upon release
  • Your team lacks DevOps resources for infrastructure management
  • Budget allows for OpenAI’s premium pricing model
  • You value tight integration over flexibility
  • Compliance allows cloud-based AI processing
  • You’re comfortable with platform limitations for ease of use

Choose Dify If:

  • You need to use multiple LLM providers or specific models
  • Data sovereignty and privacy are critical requirements
  • You want complete control over your AI infrastructure
  • Your organization requires self-hosted solutions
  • Cost optimization through model flexibility is important
  • You have DevOps capability for self-hosting
  • You need extensive RAG and document processing capabilities
  • Open-source transparency is a requirement
  • You want to avoid vendor lock-in
  • Your use case requires deep customization
  • You’re in a regulated industry (banking, healthcare, government)
  • You prefer community-driven development

🔮 Future Outlook & Roadmap

OpenAI AgentKit Roadmap

OpenAI plans to add standalone Workflows API and agent deployment options to ChatGPT. Expect rapid iteration and new features as the platform matures beyond beta stage.

Dify Development

Active open-source development with regular releases. Community-driven feature requests and transparent roadmap on GitHub. Continuous improvements to RAG, workflows, and integrations.

Market Competition

Both platforms face competition from LangChain, n8n, Zapier Central, and others. The AI agent space is rapidly evolving with new players entering regularly.

Convergence Trends

Expect features to converge over time as both platforms mature. Visual builders, multi-agent orchestration, and evaluation tools are becoming industry standards.

🎓 Final Recommendation

For most organizations: The choice depends on your priorities. If you value speed, simplicity, and are committed to OpenAI, AgentKit offers the fastest path to production agents. If you need flexibility, data control, and multi-provider support, Dify provides superior long-term value despite requiring more initial setup.

Hybrid Approach: Some organizations use AgentKit for rapid prototyping and Dify for production deployments where data sovereignty and model flexibility matter. This combines the speed of AgentKit with the control of Dify.

Last Updated: October 2025 | Based on OpenAI DevDay 2025 announcements

Sources: Official OpenAI documentation, Dify GitHub repository, TechCrunch, VentureBeat, Medium technical analyses

This comparison is for informational purposes. Features and pricing subject to change. Always consult official documentation for the most current information.

 

Building Intelligent AI Agents with OpenAI: From Raw API to Official Agents SDK

Introduction

Artificial Intelligence agents are revolutionizing how we interact with technology. Unlike traditional chatbots that simply respond to queries, AI agents can understand context, make decisions, and use tools to accomplish complex tasks autonomously. This project demonstrates how to build progressively sophisticated AI agents using both the OpenAI API and the official OpenAI Agents SDK.

Whether you’re a beginner exploring AI development or an experienced developer looking to integrate intelligent agents into your applications, this sample project provides practical, hands-on examples comparing two approaches: custom implementation using raw OpenAI API and using the official Agents SDK.

What is an AI Agent?

An AI agent is an autonomous system powered by a language model that can:

  • Understand natural language instructions
  • Make intelligent decisions about which tools to use
  • Execute functions to interact with external systems
  • Reason about results and provide meaningful responses
  • Collaborate with other agents to solve complex problems

Think of it as giving your AI assistant a toolbox. Instead of just talking, it can now check the weather, perform calculations, search databases, and much more.

Project Overview

The OpenAI AgentKit Sample Project demonstrates six levels of AI agent sophistication across two implementation approaches:

OpenAI API Approach (Custom Implementation)

1. Basic Agent

A foundational implementation showing how to set up OpenAI’s Chat Completions API.

What you’ll learn:

  • Setting up the OpenAI client
  • Configuring system and user messages
  • Managing model parameters (temperature, tokens)
  • Handling API responses

2. Agent with Tools

Introduces function calling where the agent decides when and how to use specific tools.

Available Tools:

  • Weather Tool: Retrieves current weather information
  • Calculator Tool: Performs mathematical operations
  • Time Tool: Gets current date and time across timezones

3. Advanced Agent

Production-ready example with sophisticated features including detailed logging, error handling, and multiple complex tools.

Enhanced Capabilities:

  • Wikipedia search integration
  • Sentiment analysis
  • Timezone-aware time retrieval
  • Comprehensive error handling
  • Performance statistics and logging

OpenAI Agents SDK Approach (Official Framework)

4. SDK Basic Agent

Simple agent using the official OpenAI Agents SDK with automatic agent loop and simplified API.

Key Features:

  • Uses Agent and run from @openai/agents
  • Automatic conversation management
  • Clean, minimal code

5. SDK Agent with Tools

Agent with tools using proper SDK conventions and automatic schema generation.

Tools:

  • Weather lookup with Zod validation
  • Mathematical calculations
  • Time zone support

Key Features:

  • Tools defined with tool() helper
  • Zod-powered parameter validation
  • Automatic schema generation from TypeScript types

6. SDK Multi-Agent System

Sophisticated multi-agent system with specialized agents and handoffs.

Agents:

  • WeatherExpert: Handles weather queries
  • MathExpert: Performs calculations
  • KnowledgeExpert: Searches knowledge base
  • Coordinator: Routes requests to specialists

Technology Stack

OpenAI API
GPT-4o-mini model for intelligent responses
@openai/agents
Official OpenAI Agents SDK
Zod
Runtime type validation and schema generation
Node.js
Runtime environment (22+ required for SDK)
Express.js
Web server framework
dotenv
Environment variable management

Getting Started

Prerequisites

Installation

1. Clone or download the project

cd openai-agentkit-sample

2. Install dependencies

npm install

This will install:

  • openai – Raw OpenAI API client
  • @openai/agents – Official Agents SDK
  • zod – Schema validation
  • Other dependencies

3. Configure environment variables

cp .env.example .env

Edit .env and add your OpenAI API key:

OPENAI_API_KEY=sk-your-actual-api-key-here

Running the Examples

Start the web server:

npm start

Open http://localhost:3000 in your browser

Run OpenAI API examples:

npm run example:basic      # Basic agent
npm run example:tools      # Agent with tools
npm run example:advanced   # Advanced agent

Run OpenAI Agents SDK examples:

npm run example:sdk-basic  # SDK basic agent
npm run example:sdk-tools  # SDK with tools
npm run example:sdk-multi  # Multi-agent system

Comparing the Two Approaches

OpenAI API (Custom Implementation)

Pros:

  • Full control over every aspect
  • Deep understanding of agent mechanics
  • Maximum flexibility
  • No framework constraints

Cons:

  • More code to write and maintain
  • Manual agent loop implementation
  • Manual tool schema definition
  • More error-prone

Example – Tool Definition (Raw API):

const weatherTool = {
  type: 'function',
  function: {
    name: 'get_weather',
    description: 'Get the current weather in a given location',
    parameters: {
      type: 'object',
      properties: {
        location: {
          type: 'string',
          description: 'The city and country',
        },
        unit: {
          type: 'string',
          enum: ['celsius', 'fahrenheit'],
        },
      },
      required: ['location'],
    },
  },
};
// Manual tool execution
function executeFunction(functionName, args) {
  switch (functionName) {
    case 'get_weather':
      return getWeather(args.location, args.unit);
    // ... more cases
  }
}

OpenAI Agents SDK (Official Framework)

Pros:

  • Less code, faster development
  • Automatic agent loop
  • Automatic schema generation from Zod
  • Built-in handoffs for multi-agent systems
  • Production-ready patterns
  • Type-safe with TypeScript

Cons:

  • Less control over internals
  • Framework learning curve
  • Tied to SDK conventions
  • Node.js 22+ requirement

Example – Tool Definition (Agents SDK):

import { tool } from '@openai/agents';
import { z } from 'zod';
const getWeatherTool = tool({
  name: 'get_weather',
  description: 'Get the current weather for a given location',
  parameters: z.object({
    location: z.string().describe('The city and country'),
    unit: z.enum(['celsius', 'fahrenheit']).optional().default('celsius'),
  }),
  async execute({ location, unit }) {
    // Tool implementation
    return JSON.stringify({ temperature: 22, condition: 'Sunny' });
  },
});
// Automatic execution - no switch statement needed!
const agent = new Agent({
  tools: [getWeatherTool],
});

Key Concepts

Function Calling / Tool Usage

Both approaches support function calling, where the AI model can “call” functions you define:

  1. Define tool: Describe function, parameters, and purpose
  2. Model decides: Model automatically decides when to use tools
  3. Execute tool: Your code executes the function
  4. Return result: Send result back to model
  5. Final response: Model uses result to create answer

OpenAI Agents SDK Advantages

The Agents SDK provides several powerful features:

Automatic Schema Generation:

// SDK automatically generates JSON schema from Zod!
z.object({
  city: z.string(),
  unit: z.enum(['celsius', 'fahrenheit']).optional(),
})

Agent Handoffs:

const coordinator = new Agent({
  handoffs: [weatherAgent, mathAgent, knowledgeAgent],
});
// Coordinator can automatically route to specialists

Built-in Agent Loop:

// SDK handles the entire conversation loop
const result = await run(agent, "What's the weather in Hanoi?");
console.log(result.finalOutput);

Practical Use Cases

Customer Service Automation

  • Answer questions using knowledge bases
  • Check order status
  • Process refunds
  • Escalate to human agents
  • Route to specialized agents

Personal Assistant Applications

  • Schedule management
  • Email drafting
  • Research and information gathering
  • Task automation
  • Multi-task coordination

Data Analysis Tools

  • Query databases
  • Generate reports
  • Perform calculations
  • Visualize insights
  • Collaborate across data sources

Best Practices

1. Clear Tool Descriptions

Make function descriptions detailed and specific:

Good:
description: 'Get the current weather including temperature, conditions, and humidity for a specific city and country'
Bad:
description: 'Get weather'

2. Use Zod for Validation (SDK)

parameters: z.object({
  email: z.string().email(),
  age: z.number().min(0).max(120),
  role: z.enum(['admin', 'user', 'guest']),
})

3. Error Handling

Always implement comprehensive error handling:

async execute({ city }) {
  try {
    const result = await weatherAPI.get(city);
    return JSON.stringify(result);
  } catch (error) {
    return JSON.stringify({ error: error.message });
  }
}

4. Tool Modularity

Create small, focused tools rather than monolithic ones:

// Good - specific tools
const getWeatherTool = tool({...});
const getForecastTool = tool({...});
// Bad - one giant tool
const weatherAndForecastAndHistoryTool = tool({...});

Multi-Agent Patterns

The Agents SDK excels at multi-agent workflows:

Specialist Pattern

const weatherExpert = new Agent({
  name: 'WeatherExpert',
  tools: [getWeatherTool],
});
const mathExpert = new Agent({
  name: 'MathExpert',
  tools: [calculateTool],
});
const coordinator = new Agent({
  handoffs: [weatherExpert, mathExpert],
});

Hierarchical Delegation

  • Coordinator receives user request
  • Analyzes which specialist is needed
  • Hands off to appropriate agent
  • Aggregates results
  • Returns unified response

API Endpoints

The project includes a web server with both approaches:

Raw API:

  • POST /api/chat/basic – Basic chat completion
  • POST /api/chat/with-tools – Manual tool handling

Agents SDK:

  • POST /api/chat/agents-sdk – SDK-powered agent with tools

When to Use Which Approach?

Use OpenAI API (Custom Implementation) When:

  • You need full control and customization
  • Learning how agents work at a low level
  • Implementing highly custom logic
  • Working with existing codebases
  • Framework constraints are a concern

Use OpenAI Agents SDK When:

  • Building production applications quickly
  • Need multi-agent workflows
  • Want type-safe tool definitions
  • Prefer less boilerplate code
  • Following best practices matters
  • Team collaboration is important

Performance Considerations

  • Model Selection: GPT-4o-mini offers great balance of capability and cost
  • Caching: Consider caching frequent queries
  • Async Operations: Use Promise.all() for parallel tool execution
  • Response Streaming: Implement for better UX
  • Rate Limiting: Monitor and manage API rate limits

Troubleshooting

Issue: “Invalid API Key”

  • Verify .env file contains correct API key
  • Check key is active in OpenAI dashboard

Issue: Tools Not Being Called

  • Ensure tool descriptions are clear and specific
  • Try more explicit user prompts
  • Check parameter schemas are correctly formatted

Issue: “Unsupported tool type”

  • Use tool() helper with Agents SDK
  • Ensure Zod schemas are properly defined
  • Check you’re importing from @openai/agents

Resources

Comparison Table

Feature Raw OpenAI API Agents SDK
Code Lines ~200 for basic agent with tools ~50 for same functionality
Schema Definition Manual JSON Automatic from Zod
Agent Loop Manual implementation Built-in
Type Safety Limited Full TypeScript support
Multi-Agent Manual implementation Built-in handoffs
Learning Curve Steep Moderate
Flexibility Maximum High
Production Ready Requires work Out-of-the-box
Node.js Requirement 18+ 22+

Conclusion

This project demonstrates two powerful approaches to building AI agents:

  1. Raw OpenAI API: Provides deep understanding and maximum control. Perfect for learning and custom implementations.
  2. OpenAI Agents SDK: Offers productivity, type safety, and production-ready patterns. Ideal for building real applications quickly.

Both approaches have their place. Start with the SDK for production work, but understanding the raw API approach gives you insights into how agents actually work.

Next Steps

  1. Experiment: Run all six examples
  2. Compare: Notice the differences in code complexity
  3. Customize: Create your own tools
  4. Integrate: Connect real APIs
  5. Deploy: Move to production with proper error handling
  6. Scale: Implement multi-agent systems for complex tasks

Contributing

Contributions, suggestions, and improvements are welcome! Feel free to:

  • Report issues
  • Submit pull requests
  • Share your custom tools
  • Suggest new examples

Demo

Github : https://github.com/cuongdvscuti/openai-agentkit-scuti

License

MIT License – Feel free to use this project for learning, development, or commercial purposes.


Ready to build your own AI agents?
Clone the repository, follow the setup instructions, and start with whichever approach fits your needs. The future of intelligent automation is in your hands!

AgentKit vs Dify: A Comprehensive Analysis for AI Agent Development

I. Introduction

In the rapidly evolving landscape of AI agent development, two prominent platforms have emerged as key players: AgentKit by OpenAI and Dify as an open-source alternative. This comprehensive analysis explores their capabilities, differences, and use cases to help developers and businesses make informed decisions.

II. What is AgentKit?

AgentKit is OpenAI’s comprehensive toolkit for building AI agents, designed to provide developers with the tools and infrastructure needed to create sophisticated AI-powered applications. It represents OpenAI’s vision for the future of AI agent development, offering both foundational components and advanced capabilities.

Core Components

  • Agent Builder: Visual interface for creating and configuring AI agents
  • ChatKit: Pre-built chat interfaces and conversation management
  • Connector Registry: Library of pre-built integrations with popular services
  • Evals: Comprehensive evaluation framework for testing agent performance
  • Guardrails: Safety and compliance tools for production deployments

III. What is Dify?

Dify is an open-source platform that enables users to build AI applications without extensive coding knowledge. It focuses on providing a visual, user-friendly interface for creating AI-powered workflows and applications.

Key Features

  • Visual Workflow Builder: Drag-and-drop interface for creating AI workflows
  • Multi-Model Support: Integration with various AI models and providers
  • Template Library: Pre-built templates for common use cases
  • API Management: RESTful APIs for integration

IV. Detailed Comparison: AgentKit vs Dify

Feature AgentKit Dify
Target Audience Developers & Enterprises Non-technical users & Startups
Learning Curve Steep (requires coding knowledge) Gentle (visual interface)
Customization Level High (full code control) Medium (template-based)
Integration Depth Deep API integration Surface-level integration
Scalability Enterprise-grade Small to medium projects
Cost Model Usage-based pricing Open-source + hosting costs
Support Enterprise support Community-driven
Deployment Cloud-first Self-hosted or cloud
Security Built-in enterprise security Basic security features
Performance Optimized for production Suitable for prototyping

Table 1: Feature Comparison Overview

V. Technical Implementation Comparison

Architecture and Deployment

Aspect AgentKit Dify
Architecture Microservices, cloud-native Monolithic, containerized
Deployment OpenAI cloud platform Self-hosted or cloud
Scaling Auto-scaling, enterprise-grade Manual scaling, limited
Monitoring Advanced analytics and logging Basic monitoring
Backup Automated, enterprise backup Manual backup solutions

Table 2: Architecture and Deployment Comparison

Security and Compliance

Security Feature AgentKit Dify
Authentication Enterprise SSO, MFA Basic auth, OAuth
Data Encryption End-to-end encryption Basic encryption
Compliance SOC 2, GDPR, HIPAA Basic compliance
Audit Logging Comprehensive audit trails Limited logging
Access Control Role-based, fine-grained Basic permission system

Table 3: Security and Compliance Comparison

Performance and Optimization

Metric AgentKit Dify
Response Time < 100ms (optimized) 200-500ms (standard)
Throughput 10,000+ requests/second 1,000 requests/second
Concurrent Users Unlimited (auto-scaling) Limited by infrastructure
Uptime 99.9% SLA Depends on hosting
Caching Advanced caching strategies Basic caching

Table 4: Performance and Optimization Comparison

VI. Cost and ROI Analysis

AgentKit Cost Analysis

Initial Costs

  • Setup and configuration: $5,000 – $15,000 USD
  • Team training: $10,000 – $25,000 USD
  • Integration development: $20,000 – $50,000 USD

Monthly Operating Costs

  • API usage: $0.01 – $0.10 USD per request
  • Enterprise support: $2,000 – $10,000 USD/month
  • Infrastructure: $1,000 – $5,000 USD/month

ROI Timeline: 6-12 months for enterprise projects

Dify Cost Analysis

Initial Costs

  • Setup: $0 USD (open source)
  • Basic configuration: $500 – $2,000 USD
  • Custom development: $2,000 – $10,000 USD

Monthly Operating Costs

  • Hosting: $100 – $1,000 USD/month
  • Maintenance: $500 – $2,000 USD/month
  • Support: Community-based (free)

ROI Timeline: 1-3 months for small projects

VII. Getting Started (Terminal Walkthrough)

The following screenshots demonstrate the complete setup process from scratch, showing each terminal command and its output for easy replication.

Step 1 — Clone the repository

Shows the git clone command downloading the AgentKit sample repository from GitHub with progress indicators and completion status.

Step 2 — Install dependencies

Displays the npm install process installing required packages (openai, express, cors, dotenv) with dependency resolution and warnings about Node.js version compatibility.

Step 3 — Configure environment (.env)

Demonstrates creating the .env file with environment variables including OPENAI_API_KEY placeholder and PORT configuration.

Step 4 — Run the server

Shows the server startup process with success messages indicating the AgentKit sample server is running on localhost:3000 with available agents and tools.

Step 5 — Verify health endpoint

Displays the API health check response using PowerShell’s Invoke-WebRequest command, showing successful connection and server status.

Step 6 — Verify port (optional)

Shows netstat command output confirming port 3000 is listening and ready to accept connections.

VIII. Demo Application Features

The following screenshots showcase the key features of our AgentKit sample application, demonstrating its capabilities and user interface.

Main Interface

Shows the main application interface with agent selection dropdown, tools toggle, chat messages area, and input section with modern gradient design.

Agent Switching

Demonstrates switching between different agent types (General, Coding, Creative) with dynamic response styles and specialized capabilities.

Tool Integration

Shows the calculator tool in action, displaying mathematical calculations with formatted results and tool usage indicators.

Conversation Memory

Illustrates conversation history and context awareness, showing how the agent remembers previous interactions and maintains coherent dialogue.

Mobile Responsive

Displays the mobile-optimized interface with responsive design, touch-friendly controls, and adaptive layout for smaller screens.

Error Handling

Shows graceful error handling with user-friendly error messages, retry options, and fallback responses for failed requests.

IX. Conclusion

Key Takeaways

  • AgentKit is ideal for enterprise applications requiring high performance, security, and scalability
  • Dify is perfect for rapid prototyping, small projects, and teams with limited technical expertise
  • Both platforms have their place in the AI development ecosystem
  • Choose based on your specific requirements, team capabilities, and budget constraints

The choice between AgentKit and Dify ultimately depends on your specific needs, team capabilities, and project requirements. AgentKit offers enterprise-grade capabilities for complex, scalable applications, while Dify provides an accessible platform for rapid development and prototyping.

As the AI agent development landscape continues to evolve, both platforms will likely see significant improvements and new features. Staying informed about their capabilities and roadmaps will help you make the best decision for your projects.

This analysis provides a comprehensive overview to help you choose the right platform for your AI agent development needs. Consider your specific requirements, team capabilities, and long-term goals when making your decision.

 

Multi Agent System in AI

Multi-Agent System (MAS) is a computational system where multiple agents, interact with each other and with their environment to achieve their individual or collective goals. Unlike single-agent systems where only one agent makes decisions, in MAS agents works by cooperation, competition or coordination with each other. It is widely used in complex models, distributed and dynamic problems that are too difficult for a single agent to solve alone.

The main components of Multi-Agent system are:

  • Agents: These are the individual parts of the system. Each agent has its own abilities, knowledge and goals. Agents can range from simple bots to advanced robots that can learn and adapt.
  • Environment: This is the space where agents operate. It can be a physical place like a factory or a virtual one like a digital platform. The environment shapes how agents act and interact.
  • Interactions: Agents interact with each other and the environment through various methods such as talking to each other, working together or competing. These interactions are crucial for the system to work and improve.
  • Communication: Agents often need to communicate to share information, negotiate or coordinate their actions. Effective communication helps agents work together or compete more effectively.

Architectures of Multi-Agent Systems

MAS can be designed using different architectures which define how agents are structured and how they make decisions:

1. Reactive Architecture

  • Agents respond directly to stimuli from the environment without deep reasoning.
  • Example: Obstacle-avoiding robots.

2. Deliberative (Cognitive) Architecture

  • Agents maintain internal models, perform planning, reasoning and goal selection before acting.
  • Example: Intelligent personal assistants.

3. Hybrid Architecture

  • Combines reactive and deliberative approaches. Here agents can quickly react when necessary but also plan long-term.
  • Example: Autonomous vehicles.

Types of Multi-Agent Systems

Let’s see the types of Multi-Agent Systems:

1. Cooperative MAS

  • Agents in these systems work together to achieve a common goal.
  • They share information and resources to do things that would be hard for a single agent.
  • Example: Multiple drones conducting a search-and-rescue mission.

2. Competitive MAS

  • Agents have conflicting goals and compete for limited resources.
  • Example: In competitive gaming, players (agents) compete to win.

3. Hierarchical MAS

  • These systems have a structured organization with agents at different levels.
  • Higher-level agents manage and coordinate lower-level ones.
  • Example: Mission control systems in space exploration.

4. Heterogeneous MAS

  • In these systems, agents have different skills or roles which can make the system more flexible and adaptable.
  • Example: Mixed robot teams (flying drones + ground robots).

Structures of Multi-Agent Systems (MAS)

The structural organization of a Multi-Agent System defines how agents are arranged, how they cooperate or coordinate and how control or decision-making flows within the system. This structure greatly influences the system’s efficiency, responsiveness and scalability. The main MAS structures include:

1. Flat Structure

In a flat MAS, all agents operate independently with equal status and none have authority over others. Agents communicate and interact as peers, collaborating or competing without any hierarchy. This structure promotes decentralization and flexibility, allowing agents to quickly adapt to changes.

  • Advantages: Simple to implement, robust since no single agent controls the system, avoids bottlenecks.
  • Typical Use: Peer-to-peer networks, swarm robotics, decentralized sensor networks.

2. Hierarchical Structure

Agents are organized into multiple layers or levels, forming a clear chain of command. Higher-level agents act as supervisors or coordinators, managing and delegating tasks to lower-level agents which focus on execution. This structure helps enforce order, coordination and goal alignment.

  • Advantages: Efficient task delegation, easier management of complex systems, clear responsibility separation.
  • Typical Use: Industrial control systems organizational management in enterprises, military command systems.

3. Holonic Structure

The holonic approach groups agents into holons units that are both autonomous agents themselves and parts of a higher-level agent. Each holon can act independently while also cooperating as part of a larger system. This structure supports modularity and scalability, as holons can be nested or reorganized dynamically.

  • Advantages: Flexible task allocation, supports complex systems with multiple levels of abstraction, resilient to failures.
  • Typical Use: Manufacturing systems, robot teams with sub-teams, complex adaptive systems.

4. Organizational or Network Structure

Agents are organized into networks or coalitions based on task requirements or shared goals. Agents form clusters, teams or coalitions where they share resources and coordinate to complete specific tasks. Unlike strict hierarchies, authority may be distributed based on roles or situational needs.

  • Advantages: Dynamic team formations, efficient resource sharing, adaptable to varying task demands.
  • Typical Use: Collaborative problem solving, distributed sensor networks, multi-robot coordination in logistics.

Behavior of Multi-Agent Systems

1. Autonomous Behavior

  • Agents act independently and make decisions based on their own knowledge and goals.
  • No external control is needed for their actions.

2. Cooperative Behavior

  • Agents work together to achieve shared goals.
  • They share information, divide tasks and coordinate efforts.

3. Competitive Behavior

  • Agents have conflicting goals and compete for limited resources.
  • Decision-making involves strategy and anticipation of others actions.

4. Adaptive Behavior

  • Agents learn from experience and environmental feedback.
  • They improve performance by updating strategies over time.

5. Emergent Behavior

  • Complex system-wide patterns emerge from simple local agent interactions.
  • No central control like a swarm intelligence of ant colonies or bird flocking.

Applications of Multi-Agent Systems

  • Robotics and Automation: Multiple robots cooperating in warehouses, rescue missions or exploration.
  • Smart Cities and Traffic Control: Intelligent traffic lights and vehicles coordinating to reduce congestion.
  • Economics and Trading: Autonomous trading agents in stock markets.
  • Healthcare: Coordinating hospitals, clinics and patients for resource optimization.
  • Gaming and Entertainment: Smarter NPCs and dynamic game environments.
  • Cybersecurity: Intrusion detection systems using distributed agents to monitor networks.

Advantages of MAS

  • Decentralization: No single point of failure hence becoming robust and resilient.
  • Scalability: New agents can be added without major redesign.
  • Flexibility: Handles dynamic and uncertain environments.
  • Efficiency: Workload can be distributed among multiple agents.
  • Emergent Intelligence: Complex behavior emerges from simple interaction rules.

Challenges of MAS

  • Coordination Complexity: Aligning actions of multiple agents is complex.
  • Communication Overhead: Inefficient communication may slow down the system.
  • Conflict Resolution: Agents with competing goals may reduce efficiency.
  • Scalability Issues: As the number of agents increases, managing them gets harder.
  • Security and Trust: Systems must defend against malicious or unreliable agents.

Reference linking

Event-Driven Multi-Agent Systems: Let Agents Act, Not Wait

AI is no longer just about single-use automation. The real power lies in multi-agent systems, networks of AI agents that work together, each specializing in a task but coordinating as part of a larger, intelligent system.

The fastest way to turn promising multi-agent prototypes into production systems is to make them event-driven. Replace brittle request/response chains with a shared event log and topic-based messaging so agents can react in real time, scale independently, and recover from failure by replay. Four field-tested patterns—orchestrator-worker, hierarchical, blackboard, and market-based—map cleanly onto streams (e.g., Kafka topics) and solve most coordination problems you’ll hit in the wild.

The Challenges of Multi-Agent Collaboration

AI agents don’t operate in isolation.

They need to share context, coordinate actions, and make real-time decisions — all while integrating with external tools, APIs, and data sources. When communication is inefficient, agents end up duplicating work, missing critical updates from upstream agents, or worse, creating bottlenecks that slow everything down.

Beyond communication, multi-agent systems introduce additional scaling challenges:

  • Data Fragmentation — Agents need access to real-time data, but traditional architectures struggle with ensuring consistency without duplication or loss.
  • Scalability and Fault Tolerance — As the number of agents grows, failures become more frequent. A resilient system must adapt without breaking.
  • Integration Overhead — Agents often need to interact with external services, databases, and APIs, but tightly coupled architectures make this difficult to scale.
  • Delayed Decision-Making — Many AI-driven applications, from fraud detection to customer engagement, require real-time responsiveness. But conventional request/response architectures slow this down.

 

Why multi-agent systems struggle in production

Multi-agent AI shines when specialized agents collaborate: one reasons over intent, another calls tools, another validates outputs, another enforces policy. But the moment you wire them together with synchronous calls, you create tight coupling, cascading timeouts, and opaque failure modes—exactly the problems early microservices faced before they moved to events. Agents need to react to what happened, not block each other waiting for RPCs.

Key pain points you’ll see at scale:

  • Communication bottlenecks and tangled dependencies

  • Data staleness and inconsistent context across agents

  • Fragile scaling & fault tolerance when agents come and go

  • Debuggability—it’s hard to reconstruct “who did what, when, and why” without an immutable log of event

These are precisely what event-driven design addresses.

Core idea: Agents as event processors + a shared log

Switch the mental model from “agents calling agents” to agents that consume commands/events and emit new events. Give them:

  • Input: subscriptions to topics (events/commands)

  • Processing: reasoning + tool use + retrieval over state

  • Output: new events (facts, decisions, tool results) appended to the log

With a durable, immutable event log (e.g., Kafka), you gain replay, time-travel debugging, and fan-out (many agents can react to the same event). Loose coupling drops operational complexity and lets you add/remove agents without re-wiring peers.

Four event-driven patterns you can ship today

These patterns come from distributed systems and MAS research, adapted to an event streaming backbone. Use them as building blocks rather than a religion—most real systems combine two or more.

1. Orchestrator-Worker

A central orchestrator breaks work into tasks and publishes them to a commands topic using a keying strategy (e.g., by session or customer). Workers form a consumer group, pull tasks, and publish results to a results topic. Scaling up = adding workers; failure recovery = replay from the last committed offset.

Use when: you need ordered handling per key, clear ownership of “who decides next,” and easy horizontal scale.

2. Hierarchical Agents

A tree of orchestrators: higher-level agents decompose goals into sub-goals for mid-level agents, which orchestrate leaf agents. Each layer is just a specialized orchestrator-worker pattern with its own topics, so you can evolve the tree without bespoke glue code.

Use when: problems decompose naturally (e.g., “Plan → Research → Draft → Review → Approve”).

3. Blackboard (Shared Memory)

Agents collaborate by reading/writing to a shared blackboard topic (or set of topics). Instead of point-to-point calls, each agent posts partial findings and subscribes to the evolving “state of the world.” Add lightweight schema tags (origin, confidence, step) for downstream filtering.

Use when: contributions are incremental and loosely ordered (perception → hypotheses → refinement).

4. Market-Based (Bidding)

Agents “bid” on a task by posting proposals; an aggregator selects winners after N rounds. Moving bids and awards onto topics prevents the O(N²) web of direct connections between solvers and keeps negotiation auditable.

Use when: you want competition among diverse solvers (planning, routing, pricing, ensemble reasoning).

Architecture sketch

At minimum you’ll want:

  1. Topics: agent.commands.*, agent.events.*, agent.results.*, plus domain streams (orders, alerts, leads).

  2. Schemas: JSON/Avro with versioned envelopes (type, source_agent, correlation_id, causation_id, ttl, safety_level, confidence).

  3. State: local caches or stateful processors (Flink/ksqlDB) for per-key context, backed by a durable changelog.

  4. Governance: central registry for schemas, PII tags, retention, and ACLs; redaction at the edge.

  5. Observability: trace by correlation_id; attach decision summaries to each event for auditability and evals.

From request/response to events: a practical migration path

  1. Define the agent interface as events. List the event types each agent consumes and emits. Treat these as public contracts.

  2. Introduce topics alongside your existing RPCs. Start publishing key milestones (task-created, tool-called, output-ready) even while calls remain.

  3. Move coordination out of code and into the stream. Replace “call Agent B, wait” with “publish Need:SummaryDraft and subscribe to SummaryDrafted.”

  4. Add replay-based testing. Re-feed yesterday’s log into a staging cluster to regression-test new agent policies without touching prod.

  5. Evolve toward patterns. As volume and agent count grow, snap into orchestrator-worker or blackboard to keep complexity in check.

Real-world payoffs

  • Parallelism: multiple agents respond to the same event—no coordinator bottleneck.

  • Resilience: if one agent dies, events aren’t lost; it resumes from the last offset.

  • Adaptability: add a new “critic” or “safety” agent by subscribing it to existing topics.

  • Traceability: every decision is a line in the log; audits and RCA stop being archaeology.

Pitfalls & how to avoid them

  • Schema drift → Use a schema registry and contract testing; never break consumers.

  • Unbounded topics → Set retention & compaction by domain (minutes for hot signals, days for ops, long-term in the data lake).

  • Chatty agents → Introduce back-pressure (quotas), batch low-value events, and enforce ttl.

  • Hidden coupling → If an agent can’t act without a specific peer, you’ve snuck in a request/response dependency. Refactor to events.

Example: Minimal event envelope (pseudocode)

When to pick which pattern

  • Highly structured workflowsOrchestrator-Worker

  • Goal decompositionHierarchical

  • Collaborative sense-makingBlackboard

  • Competitive ensemble solvingMarket-Based

In practice, start orchestrator-worker for reliability, add a blackboard for shared context, then scale into hierarchical as teams/features grow

The bottom line

If you’re serious about production-grade agents, architecture matters more than model choice. Event-driven design gives agents the freedom to act while staying coordinated, observable, and resilient—mirroring the same evolution that made microservices workable at scale. Now is the time to formalize your agent interfaces as events and adopt patterns that have already proven themselves in distributed systems.

Further reading

  • Four Design Patterns for Event-Driven, Multi-Agent Systems (Confluent, Feb 19, 2025). Clear, concrete mappings of MAS patterns onto Kafka. Confluent

  • AI Agents Must Act, Not Wait (Medium, Jul 9, 2025). A crisp case for event-driven MAS and the shift away from request/response. Medium