Author: Long Dang

Serverless generative AI architectural patterns – Part 1

Posted on by Long Dang

As organizations explore how to embed generative AI capabilities into their applications, many are leveraging large language models (LLMs) for tasks like content generation, summarization, or natural language interfaces. However, designing these systems for scalability, cost-efficiency, and agility can be challenging.

This blog post (Part 1 of a two-part series) introduces serverless architectural patterns for building real-time generative AI applications using AWS services. It provides guidance on design layers, execution models, and implementation considerations.

📐 Separation of Concerns: A 3-Tier Design

To manage complexity and improve maintainability, AWS recommends separating your application into three distinct layers:

1. Frontend Layer – User Experience and Interaction

This layer manages user-facing interactions, including UI rendering, authentication, and client-to-server communication.

Tools and Services:

  • AWS Amplify: For rapid frontend development with built-in CI/CD.

  • Amazon CloudFront + S3: To host static sites securely and at scale.

  • Amazon Lex: To build conversational interfaces.

  • Amazon ECS/EKS: If using containerized web applications.

2. Middleware Layer – Integration and Control Logic

This is the central control hub and is subdivided into three critical sub-layers:

  • API Layer:

    • Interfaces via REST, GraphQL, or WebSockets.

    • Ensures secure, scalable access via API Gateway, AWS AppSync, or ALB.

    • Manages versioning, rate-limiting, authentication.

  • Prompt Engineering Layer:

    • Builds reusable prompt templates.

    • Handles prompt versioning, moderation, security, and caching.

    • Integrates with services like Amazon Bedrock, Amazon DynamoDB, and Amazon ElastiCache.

  • Orchestration Layer:

    • Manages session context, multi-step workflows, and agent-based processing.

    • Uses tools like AWS Step Functions, Amazon SQS, or event-driven orchestration frameworks such as LangChain or LlamaIndex.

3. Backend Layer – LLMs, Agents, and Data

This is where the actual generative AI models and enterprise data reside.

LLM Hosting Options:

  • Amazon Bedrock: Fully managed access to foundation models.

  • Amazon SageMaker: For training or hosting custom models.

  • Model Context Protocol (MCP): For containerized model servers.

For Retrieval Augmented Generation (RAG):

  • Amazon OpenSearch, Amazon Kendra, or Amazon Aurora PostgreSQL (pgVector) can index and retrieve relevant documents based on user queries.

⚡ Real-Time Execution Patterns

The article introduces three real-time architectural patterns to suit different UX and latency needs:

Pattern 1: Synchronous Request-Response

In this pattern, responses are generated and immediately delivered, while the client blocks/waits for response. Although this is simple to implement, has a predictable flow, and offers strong consistency, it suffers from blocking operations, high latency, and potential timeouts.

  • User sends a prompt, and the application returns a complete response.

  • Simple to implement and user-friendly for quick tasks.

  • Tradeoff: Limited by timeout constraints (e.g., API Gateway default 29s).

Use Cases:

  • Short-form responses

  • Structured data generation

  • Real-time form filling

 

This model can be implemented through several architectural approaches.

REST APIs

You can use RESTful APIs to communicate with your backend over HTTP requests. You can use REST or HTTP APIs in API Gateway or an Application Load Balancer for path-based routing to the middleware.

GraphQL HTTP APIs

You can use AWS AppSync as the API layer to take advantage of the benefits of GraphQL APIs. GraphQL APIs offer declarative and efficient data fetching using a typed schema definition, serverless data caching, offline data synchronization, security, and fine-grained access control.

Conversational chatbot interface

Amazon Lex is a service for building conversational interfaces with voice and text, offering speech recognition and language understanding capabilities. It simplifies multimodal development and enables publication of chatbots to various chat services and mobile devices.

Model invocation using orchestration

AWS Step Functions enables orchestration and coordination of multiple tasks, with native integrations across AWS services like Amazon API Gateway, AWS Lambda, and Amazon DynamoDB.

 

Pattern 2: Asynchronous Request-Response

This pattern provides a full-duplex, bidirectional communication channel between the client and server without clients having to wait for updates. The biggest advantages is its non-blocking nature that can handle long-running operations. However, they are more complex to implement because they require channel, message, and state management.

  • The request is submitted, and the response is delivered via polling or a callback.

  • Allows long-running operations without blocking client.

Implementation:

  • Uses services like Amazon SQS, SNS, or EventBridge.

  • Clients can poll or subscribe to notification mechanisms.

Use Cases:

  • Background processing

  • Multi-document summarization

  • Secure, queue-based workloads

 

This model can be implemented through two architectural approaches.

WebSocket APIs

The WebSocket protocol enables real-time, synchronous communication between the frontend and middleware, allowing for bidirectional, full-duplex messaging over a persistent TCP connection.

GraphQL WebSocket APIs

AWS AppSync can establish and maintain secure WebSocket connections for GraphQL subscription operations, enabling middleware applications to distribute data in real time from data sources to subscribers. It also supports a simple publish-subscribe model, where client frontends can listen to specific channels or topics

Pattern 3: Asynchronous Streaming Response

This streaming pattern enables real-time response flow to clients in chunks, enhancing the user experience and minimizing first response latency. This pattern uses built-in streaming capabilities in services like Amazon Bedrock

  • The client receives partial results as the model generates them.

  • Enhances user experience for chat interfaces and long-form text.

Implementation:

  • WebSocket APIs via API Gateway

  • Streaming through Amazon Bedrock

  • Lambda for function execution and streaming buffers

Use Cases:

  • Conversational AI

  • Live text generation

  • Code assistant interfaces

The following diagram illustrates the architecture of asynchronous streaming using API Gateway WebSocket APIs.

The following diagram illustrates the architecture of asynchronous streaming using AWS AppSync WebSocket APIs.

If you don’t need an API layer, Lambda response streaming lets a Lambda function progressively stream response payloads back to clients.

🧠 Choosing the Right Pattern

Each pattern serves different needs. When designing your system, consider:

  • Desired user experience (interactive vs. delayed)

  • Model latency and runtime

  • Infrastructure constraints (timeouts, resource limits)

  • API Gateway and Lambda service quotas

  • Security and compliance needs

🔜 What’s Next?

This article focused on real-time interactions. Part 2 will explore batch-oriented generative AI patterns—suitable for scenarios like document processing, analytics generation, and large-scale content creation.

Multi Agent System in AI

Posted on by Long Dang

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

Posted on by Long Dang

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 workflows → Orchestrator-Worker

  • Goal decomposition → Hierarchical

  • Collaborative sense-making → Blackboard

  • Competitive ensemble solving → Market-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

🧑‍💻 Kiro – When the AI IDE Becomes a Software Architect

Posted on by Long Dang

In recent years, AI coding assistants have reshaped how we build software. From GitHub Copilot to Cursor and Windsurf, developers can now write code faster, debug less, and “vibe code” any idea with just a few prompts.

But alongside the convenience comes a major issue: lack of structure. Prototypes are spun up quickly but are hard to scale. Code lacks documentation, design artifacts, and tests, often leading to technical debt.

Amazon AWS introduced Kiro to solve exactly this. Instead of being just a “coding companion,” Kiro positions itself as an AI software architect – guiding you from requirements to design, implementation, and validation. It marks a shift from prompt-driven development to spec-driven development.

 

🌍 Why Do We Need a “Spec-Driven AI IDE”

Most AI coding tools today focus on speed & productivity for individuals. Cursor, Copilot, and Windsurf enable a single developer to prototype an MVP in days. But when it’s time to scale or work in a team, these prototypes often become liabilities:

  • No clear requirements → miscommunication when tasks are handed off.
  • No design docs → difficult to refactor or onboard new devs.
  • Poor test coverage → bugs slip through QA.
  • Lack of best practices → fragile architectures.

AWS identified this gap and proposed a philosophy: AI should not only be a coding assistant but a software architect. Kiro is designed to follow a full development lifecycle: Requirement → Design → Implementation → Validation.

🔑 Core Features of Kiro

1. Spec-Driven Development

You describe requirements in natural language, and Kiro generates:

  • Requirement documents (user stories, acceptance criteria).
  • Design documents (system architecture, ER diagrams, sequence diagrams).
  • Task lists (implementation steps, tests).

 

2. Agent Hooks – Your “Senior Dev on Autopilot”

Agent hooks in Kiro act like a senior developer running in the background:

  • Automatically generate unit tests when you save files.
  • Check code smells against SOLID principles.
  • Update README and API specs.
  • Scan for accidentally committed secrets or credentials.

3. MCP & Steering Rules – Context Integration

Kiro supports the Model Context Protocol (MCP), allowing AI to access company docs, API specifications, or database schemas for richer context. Steering rules ensure AI stays aligned with project goals.

4. Agentic Chat – Context-Aware Conversations

Beyond inline completions, Kiro’s chat agent understands the entire codebase, requirements, and design docs. You can request:

  • “Add OAuth2 login flow with Google.”
  • “Generate a sequence diagram for password reset.”
  • “Write integration tests with Postgres.”

5. Familiar Foundation

  • Built on Code OSS, fully compatible with VS Code extensions, themes, and settings.
  • Powered by strong models (Claude 3.7, Claude 4.0) with future support for GPT and Gemini.
  • Pricing (Preview): Free tier (50 interactions/month), Pro $19 (1,000 interactions), Pro+ $39 (3,000 interactions).

⚔️ Kiro vs Cursor – A Fascinating Duel

Criteria Kiro – Spec-Driven IDE Cursor – Prompt-Driven IDE
Philosophy Requirement → Design → Code → Test Prompt → Code
Automation Agent Hooks (docs, tests, best practices) Manual, one task at a time
Context Handling MCP: APIs, DBs, external docs Mainly codebase indexing
Output Production-grade, standardized Quick prototypes, vibe coding
IDE Foundation Code OSS (VS Code ecosystem) VS Code fork
Best Fit For Enterprise teams, large-scale development Startups, solo prototyping

 

🛠️ Walkthrough: How Kiro Handles a Requirement

To see Kiro in action, I asked it to:

“Create a REST API for login/signup with JWT. You can use documents accounts for logic login/signup.”

Kiro then generated three artifacts: requirements, design, and tasks — essentially doing the work of a product manager, software architect, and tech lead in one.

You can see more in the folder .kiro in the source code on GitHub

1. Requirements

Kiro produced a requirements.md file describing the system as user stories with acceptance criteria:

  • Signup: Users can register with email and password. Validation rules include unique email, proper format, and a minimum 8-character password. On success, the system returns a JWT and user info.

  • Login: Users log in with valid credentials to receive a JWT. Invalid or non-existent accounts return a 401 Unauthorized.

  • Token Validation: Protected routes require a valid JWT. Expired, missing, or malformed tokens are rejected.

  • Security: Passwords must be hashed with bcrypt, never stored in plain text, and tokens signed with a secure secret.

  • Error Handling: The API must return clear but secure error messages, avoiding user enumeration.

This structured requirements file ensures that the authentication system has a clear scope and testable outcomes before any code is written.

2. Design

Next, Kiro generated a design.md document, laying out the NestJS architecture:

  • Modules & Services: AuthModule, AuthService, AuthController, JwtStrategy, AuthGuard.

  • DTOs for input validation (signup.dto.ts, login.dto.ts).

  • Data Model: An extended AccountSchema with unique userId/userName fields, password hashing middleware, and timestamps.

  • Security Setup: bcrypt with 12 salt rounds, JWTs signed with HS256, 24-hour expiration.

  • REST Endpoints:

    • POST /auth/signup → register new accounts

    • POST /auth/login → authenticate and return token

    • GET /auth/profile → protected endpoint returning current user info

The design document also detailed error handling policies (e.g., generic “Invalid credentials” messages), validation strategies, and a test plan (unit + integration).

3. Tasks

Finally, Kiro produced a tasks.md file — essentially an implementation plan:

  1. Setup dependencies: Install @nestjs/jwt, passport-jwt, bcrypt, and validation libraries.

  2. Create DTOs for signup/login validation.

  3. Enhance the Account model with password hashing and secure comparison methods.

  4. Implement JWT strategy for validating tokens in requests.

  5. Build AuthService methods for signup and login, returning JWTs.

  6. Build AuthController endpoints: /signup, /login, /profile.

  7. Add AuthGuard to protect routes.

  8. Wire everything into AuthModule and integrate with the app.

  9. Error handling via custom exception filters.

  10. Unit & integration tests for flows like signup, login, and token validation.

This task list reads like a well-prepared Jira board — ready for developers to pick up and implement step by step.

✨ The Result

In just one prompt, Kiro produced a requirements spec, a detailed design doc, and an actionable implementation plan.

Instead of jumping straight into code, the system starts with clarity:

  • What needs to be built

  • How it should be structured

  • How to test and validate it

This demonstrates how Kiro goes beyond “AI autocomplete” and into end-to-end engineering workflow automation.

🧪 Real-World Feedback from Early Users

1. Dev.to – Testing Kiro in Real Projects

  • Kiro produced clear design docs and structured task lists.
  • Agent Hooks auto-generated basic tests (though not deep coverage).
  • Strongest feature: spec-first workflow → immediate blueprint for the project.
  • Limitation: complex logic still requires developer intervention.

👉 Takeaway: Kiro feels more like a senior PM + junior dev than a pure coder.

2. Substack – Developing with Kiro

  • After just a few lines of description, Kiro generated detailed user stories broken into assignable tasks.
  • Docs and code stayed in sync — changes to requirements updated design and code automatically.
  • Saved several days of manual documentation work.
  • Still requires developer review for security and performance concerns.

👉 Takeaway: Perfect for small teams or startups without a dedicated product manager.

3. AWS Re:Post – Beyond a Coding Assistant

  • Positions Kiro as a tool for full-stack development from spec to deployment.
  • Biggest strength: reducing communication overhead between devs, PMs, and QA.

👉 Takeaway: The real value lies not just in code generation, but in process standardization.

🎯 Insights from Real Use Cases

  • Biggest Strength: End-to-end sync from requirements → design → code, saving huge time on documentation and planning.
  • Main Limitation: Complex logic still needs developer oversight, especially for security and performance.
  • Ideal Use Cases: Startups that need speed and structure, or enterprise teams looking to minimize technical debt.

📌 Conclusion – Is Kiro the “Future IDE”?

Kiro is not just another AI IDE. It represents a new philosophy: Spec-Driven Development, where AI doesn’t just write code but participates in the entire software development lifecycle.

  • Cursor remains fantastic when you need to code fast and iterate rapidly.
  • Kiro is for when you want to elevate AI from “assistant” to “software architect.”

💡 My take:

Kiro may not replace Cursor immediately. But in the next 2–3 years, as enterprises demand standardized, testable, documented code, spec-driven IDEs like Kiro are likely to become the norm.

👉 Have you tried Kiro yet? Do you think the future of AI IDEs should lean more towards speed (Cursor style) or structure (Kiro style)?

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