How Apache Kafka Powers the Next Generation of GenAI Applications

 

We are living in two technological revolutions simultaneously: the rise of Generative AI (GenAI) and the ubiquity of real-time data streaming with Apache Kafka. But here's the thing—they are not separate worlds. In fact, their intersection is where some of the most powerful, intelligent applications are being built.

Imagine a GenAI model that doesn't just respond to a static prompt but reacts to live data streams—customer interactions, stock market ticks, or IoT sensor readings—as they happen. That's the promise of combining GenAI with Apache Kafka. In this post, we'll explore why Kafka is becoming the backbone of modern AI architectures and how you can start building real-time AI pipelines today.

What is Apache Kafka? (A Quick Refresher)
Apache Kafka is a distributed event streaming platform. Think of it as a highly durable, scalable, and fast central nervous system for your data. It allows you to:

• Publish and subscribe to streams of events (records).

• Store streams of events durably and reliably.

• Process streams of events in real-time or retrospectively.

For years, Kafka has been the standard for moving data between systems. Now, it's becoming essential for moving data to and from AI models.

Why GenAI Needs Apache Kafka
GenAI models, especially Large Language Models (LLMs), are powerful but often operate in a static, request-response mode. They know what they were trained on, but not what's happening right now. Kafka bridges this gap.

Challenge with Standalone GenAI	How Kafka Solves It
Static Knowledge: Model only knows its training data.	Real-Time Context: Feeds live data (e.g., current inventory, latest news) into the prompt.
Batch Processing: Traditional AI often runs on batches of data.	Event-Driven AI: Models can react to events the instant they occur.
Data Silos: AI models are disconnected from operational data.	Unified Data Layer: Kafka acts as a single source of truth for all data streams.
Scalability: Handling millions of requests is hard.	Decoupling & Buffering: Kafka buffers requests, ensuring the AI service isn't overwhelmed.

Key Architecture Patterns for GenAI + Kafka
Here are three common ways developers are combining these technologies:

1. Real-Time Feature Store for RAG (Retrieval-Augmented Generation)
RAG is a technique to improve LLM responses by retrieving relevant information from a knowledge base at the moment a question is asked.

• How Kafka Helps: Kafka can stream real-time updates (e.g., new support tickets, product catalog changes) directly into the vector database that the RAG system queries. This ensures the LLM always has the freshest context.

2. Streaming Inference
Instead of sending data to a model in batches, you send it as a continuous stream.

• How It Works: An event (like a customer clicking on a website) lands in a Kafka topic. A Kafka Streams application or a Kafka consumer picks up that event, sends it to a pre-deployed GenAI model (e.g., for sentiment analysis or personalization), and the result is streamed back to another Kafka topic for downstream applications.

3. Event-Driven AI Agents
Imagine an AI agent that monitors a Kafka topic for "customer support request" events.

• How It Works: When a new request appears, the agent is triggered. It uses an LLM to draft a response, fetches order history from another Kafka topic, and posts the final answer back to a "response" topic—all in real-time.

Building a Simple Pipeline: A Conceptual Example
Let's look at a simple, high-level example using Python-like pseudocode.

Scenario: A support chatbot that needs to know a customer's recent order status to answer questions accurately.

python

# Consumer that listens for new support questions
from kafka import KafkaConsumer
import openai # Your GenAI model API

consumer = KafkaConsumer('customer-questions', bootstrap_servers='localhost:9092')

for message in consumer:
    question_data = message.value # Contains user_id and question
    
    # 1. Fetch real-time context from another Kafka topic
    order_context = get_latest_order_from_kafka(question_data['user_id'])
    
    # 2. Build a prompt with the real-time context
    prompt = f"Customer Order: {order_context}\n\nQuestion: {question_data['question']}\n\nAnswer:"
    
    # 3. Call the GenAI model
    response = openai.ChatCompletion.create(model="gpt-4", messages=[{"role": "user", "content": prompt}])
    
    # 4. Send the answer back to a response topic
    send_to_kafka('chatbot-responses', response['choices'][0]['message']['content'])
    
    print(f"Answered question with real-time order data.")

This simple pattern unlocks powerful, context-aware AI applications.

Real-World Use Cases

• Financial Services: Real-time fraud detection where an LLM analyzes a transaction stream alongside a customer's historical behavior.

• E-commerce: Personalized shopping assistants that know exactly what's in stock right now and can make recommendations based on live browsing data.

• IoT: Generative AI that describes what's happening in a factory based on a real-time stream of sensor data.

Conclusion
The combination of Generative AI and Apache Kafka is more than a trend; it's a fundamental shift towards building AI that is aware of the present moment. By using Kafka as the data backbone, you give your AI models the gift of context, enabling them to move from being simple chatbots to becoming intelligent, reactive systems embedded in the heart of your business operations.

The stream is the source of truth. It's time to let your AI drink from it.

Are you using Kafka with AI in your projects? What challenges have you faced? Share your thoughts in the comments below!

 

Build Your Own AI Agent: A Practical Guide with Kiro, Strands SDK, and Amazon Bedrock AgentCore

The era of AI agents is here. Unlike simple chatbots that only respond to queries, AI agents can take actions, use tools, access external data, and make decisions autonomously. But building production-ready agents has been complex—until now.

In this comprehensive guide, I'll walk you through building your own AI agent using three powerful modern tools:

• Kiro – An Agentic Integrated Development Environment (IDE)

• Strands Agents SDK – A lightweight framework for building agent workflows

• Amazon Bedrock AgentCore – AWS's managed service for deploying scalable agents

By the end, you'll understand how to connect LLMs to tools and external data sources, and how to move closer to deploying production-ready AI agents.

---

What Are AI Agents?

Before diving into the tools, let's clarify what makes an AI agent different from a standard LLM:

Standard LLM	AI Agent
Responds based on training data	Takes actions in real-world systems
Cannot use external tools	Can call APIs, databases, and functions
Static knowledge cutoff	Accesses live data sources
Single-turn or multi-turn chat	Multi-step reasoning and planning
No memory of past interactions	Maintains context across sessions

An AI agent combines an LLM's reasoning capabilities with tool use and memory to accomplish complex tasks.

---

The Modern AI Agent Stack

For this tutorial, we'll use three complementary tools:

1. Kiro: The Agentic IDE

Kiro is a revolutionary development environment built specifically for AI agent development. It provides:

• Visual workflow design for agent logic

• Built-in testing sandbox

• Integration with multiple LLM providers

• Version control for agent configurations

• Debugging tools with step-by-step execution tracing

2. Strands Agents SDK

Strands is a lightweight, open-source SDK that simplifies:

• Tool definition and registration

• Agent orchestration and handoffs

• Memory and state management

• Streaming responses

• Multi-agent collaboration

3. Amazon Bedrock AgentCore

Bedrock AgentCore is AWS's managed service that handles:

• Scalable agent deployment

• Automatic prompt engineering

• Action group management

• Knowledge base integration

• Security and compliance

• Cost optimization

---

Prerequisites

To follow along, you'll need:

• Node.js 18+ or Python 3.9+ (depending on your preference)

• AWS Account (for Bedrock AgentCore)

• Kiro IDE – Download from kiro.dev

• API Keys for at least one LLM provider (OpenAI, Anthropic, or AWS Bedrock)

• Basic understanding of JavaScript/Python and REST APIs

---

Step 1: Setting Up Kiro IDE

Kiro provides a visual environment for designing agent workflows.

1. Install Kiro
Download and install Kiro from the official website. Once installed, create a new project:

bash

# Kiro CLI (if available)
kiro new my-ai-agent
cd my-ai-agent
kiro dev

2. Configure Your LLM Provider
In Kiro's settings panel, add your API keys:

• OpenAI (GPT-4, GPT-3.5)

• Anthropic (Claude)

• AWS Bedrock (Titan, Claude, Llama)

3. Create Your First Agent
Using Kiro's visual designer:

• Create a new agent called CustomerSupportAgent

• Set the system prompt: "You are a helpful customer support agent for an e-commerce store. You can check order status, process returns, and answer product questions."

• Select your preferred LLM model

---

Step 2: Defining Tools with Strands SDK

Now let's create actual tools our agent can use. We'll use Strands SDK for this.

Install Strands SDK:

bash

# Using npm
npm install @strands/agents-sdk

# Using pip
pip install strands-agents-sdk

Define Tools (JavaScript/TypeScript):

javascript

// tools/orderTools.js
import { tool } from '@strands/agents-sdk';

// Tool to check order status
export const checkOrderStatus = tool({
  name: 'check_order_status',
  description: 'Check the status of a customer order',
  parameters: {
    orderId: {
      type: 'string',
      description: 'The order ID to check',
      required: true
    },
    email: {
      type: 'string',
      description: 'Customer email for verification',
      required: true
    }
  },
  execute: async ({ orderId, email }) => {
    // In production, this would call your actual order system
    // This is a mock implementation
    const mockOrders = {
      'ORD-12345': { status: 'shipped', estimatedDelivery: '2026-03-15' },
      'ORD-67890': { status: 'processing', estimatedDelivery: '2026-03-20' },
      'ORD-54321': { status: 'delivered', deliveredDate: '2026-03-05' }
    };
    
    const order = mockOrders[orderId];
    if (!order) {
      return { error: 'Order not found' };
    }
    
    return {
      orderId,
      status: order.status,
      estimatedDelivery: order.estimatedDelivery || order.deliveredDate
    };
  }
});

// Tool to initiate return
export const initiateReturn = tool({
  name: 'initiate_return',
  description: 'Start a return process for an order',
  parameters: {
    orderId: {
      type: 'string',
      description: 'The order ID to return',
      required: true
    },
    reason: {
      type: 'string',
      description: 'Reason for return',
      required: true
    },
    email: {
      type: 'string',
      description: 'Customer email',
      required: true
    }
  },
  execute: async ({ orderId, reason, email }) => {
    // Mock return initiation
    const returnId = `RET-${Math.random().toString(36).substring(7)}`;
    return {
      returnId,
      status: 'initiated',
      instructions: 'Please pack the item and drop it at any nearby post office. Shipping label will be emailed within 24 hours.'
    };
  }
});

Python Version:

python

# tools/order_tools.py
from strands import tool

@tool(
    name="check_order_status",
    description="Check the status of a customer order",
    parameters={
        "orderId": {"type": "string", "description": "The order ID to check", "required": True},
        "email": {"type": "string", "description": "Customer email for verification", "required": True}
    }
)
async def check_order_status(orderId: str, email: str):
    mock_orders = {
        'ORD-12345': {'status': 'shipped', 'estimatedDelivery': '2026-03-15'},
        'ORD-67890': {'status': 'processing', 'estimatedDelivery': '2026-03-20'},
        'ORD-54321': {'status': 'delivered', 'deliveredDate': '2026-03-05'}
    }
    
    order = mock_orders.get(orderId)
    if not order:
        return {"error": "Order not found"}
    
    return {
        "orderId": orderId,
        "status": order["status"],
        "estimatedDelivery": order.get("estimatedDelivery", order.get("deliveredDate"))
    }

---

Step 3: Connecting to External Data Sources

Modern AI agents need access to real-time data. Let's connect our agent to external data sources.

1. Database Connection (PostgreSQL Example)

javascript

// tools/databaseTools.js
import { Pool } from 'pg';
import { tool } from '@strands/agents-sdk';

const pool = new Pool({
  connectionString: process.env.DATABASE_URL
});

export const queryProducts = tool({
  name: 'query_products',
  description: 'Search for products in the inventory',
  parameters: {
    category: {
      type: 'string',
      description: 'Product category to filter by',
      required: false
    },
    minPrice: {
      type: 'number',
      description: 'Minimum price',
      required: false
    },
    maxPrice: {
      type: 'number',
      description: 'Maximum price',
      required: false
    },
    searchTerm: {
      type: 'string',
      description: 'Text to search in product names/descriptions',
      required: false
    }
  },
  execute: async (params) => {
    let query = 'SELECT * FROM products WHERE 1=1';
    const values = [];
    let paramIndex = 1;
    
    if (params.category) {
      query += ` AND category = $${paramIndex++}`;
      values.push(params.category);
    }
    
    if (params.minPrice) {
      query += ` AND price >= $${paramIndex++}`;
      values.push(params.minPrice);
    }
    
    if (params.maxPrice) {
      query += ` AND price <= $${paramIndex++}`;
      values.push(params.maxPrice);
    }
    
    if (params.searchTerm) {
      query += ` AND (name ILIKE $${paramIndex++} OR description ILIKE $${paramIndex++})`;
      const searchPattern = `%${params.searchTerm}%`;
      values.push(searchPattern, searchPattern);
    }
    
    query += ' LIMIT 10';
    
    const result = await pool.query(query, values);
    return result.rows;
  }
});

2. REST API Integration

javascript

// tools/apiTools.js
import { tool } from '@strands/agents-sdk';
import axios from 'axios';

export const getWeather = tool({
  name: 'get_weather',
  description: 'Get current weather for a location',
  parameters: {
    location: {
      type: 'string',
      description: 'City name or coordinates',
      required: true
    }
  },
  execute: async ({ location }) => {
    const apiKey = process.env.WEATHER_API_KEY;
    const response = await axios.get(
      `https://api.openweathermap.org/data/2.5/weather?q=${location}&appid=${apiKey}&units=metric`
    );
    
    return {
      location: response.data.name,
      temperature: response.data.main.temp,
      conditions: response.data.weather[0].description,
      humidity: response.data.main.humidity,
      windSpeed: response.data.wind.speed
    };
  }
});

---

Step 4: Orchestrating the Agent with Strands

Now let's bring everything together using Strands Agents SDK.

javascript

// agent/customerSupportAgent.js
import { Agent, Runner } from '@strands/agents-sdk';
import { checkOrderStatus, initiateReturn } from '../tools/orderTools';
import { queryProducts } from '../tools/databaseTools';
import { getWeather } from '../tools/apiTools';

// Create the agent
const customerSupportAgent = new Agent({
  name: 'Customer Support Agent',
  instructions: `
    You are a helpful customer support agent for an e-commerce store.
    
    You have access to these tools:
    - check_order_status: Check where an order is
    - initiate_return: Help customers return items
    - query_products: Find products in inventory
    - get_weather: Check weather (useful for delivery questions)
    
    Always be polite and helpful. If you don't know something, be honest.
    For returns, always ask for the reason before proceeding.
  `,
  tools: [
    checkOrderStatus,
    initiateReturn,
    queryProducts,
    getWeather
  ],
  model: 'gpt-4-turbo', // or 'claude-3-opus', etc.
  temperature: 0.2, // Lower temperature for consistent responses
  maxTokens: 1000
});

// Create a runner to execute the agent
const runner = new Runner({
  agent: customerSupportAgent,
  stream: true // Enable streaming responses
});

// Function to handle user queries
export async function handleUserQuery(userMessage, sessionId) {
  const response = await runner.run({
    input: userMessage,
    sessionId,
    onToolCall: (toolName, params) => {
      console.log(`Agent called tool: ${toolName} with params:`, params);
    },
    onToolResult: (toolName, result) => {
      console.log(`Tool ${toolName} returned:`, result);
    }
  });
  
  return response;
}

Using the Agent:

javascript

// app.js
import { handleUserQuery } from './agent/customerSupportAgent.js';

// Example conversation
async function main() {
  const sessionId = 'user-123';
  
  const query1 = await handleUserQuery(
    "Where is my order ORD-12345? I used email [email protected]",
    sessionId
  );
  console.log('Agent:', query1.output);
  
  const query2 = await handleUserQuery(
    "I want to return it. The product is damaged.",
    sessionId
  );
  console.log('Agent:', query2.output);
}

main();

---

Step 5: Deploying to Production with Amazon Bedrock AgentCore

Now that our agent works locally, let's deploy it to production using Amazon Bedrock AgentCore.

1. Set Up AWS Credentials

bash

aws configure
# Enter your AWS Access Key ID, Secret Access Key, and region

2. Package Your Agent for Bedrock

Create a deployment configuration:

javascript

// bedrock-config.js
export const bedrockConfig = {
  agentName: 'customer-support-agent',
  description: 'E-commerce customer support agent',
  foundationModel: 'anthropic.claude-3-sonnet-20240229-v1:0', // Bedrock model ID
  instruction: `
    You are a helpful customer support agent for an e-commerce store.
    You can check order status, process returns, and answer product questions.
    Always be polite and professional.
  `,
  actionGroups: [
    {
      name: 'OrderManagement',
      description: 'Tools for managing orders',
      tools: ['check_order_status', 'initiate_return']
    },
    {
      name: 'ProductCatalog',
      description: 'Tools for product queries',
      tools: ['query_products']
    }
  ],
  knowledgeBase: {
    id: 'KB-12345', // Optional: Link to a Bedrock Knowledge Base
    description: 'Product catalog and FAQ database'
  },
  idleSessionTTLInSeconds: 1800, // 30 minutes
};

3. Deploy Using AWS CLI

bash

# Create the agent
aws bedrock-agent create-agent \
  --agent-name customer-support-agent \
  --agent-resource-role-arn arn:aws:iam::123456789012:role/bedrock-agent-role \
  --foundation-model anthropic.claude-3-sonnet-20240229-v1:0 \
  --instruction "You are a helpful customer support agent..."

# Associate action groups
aws bedrock-agent create-agent-action-group \
  --agent-id YOUR-AGENT-ID \
  --action-group-name OrderManagement \
  --action-group-executor custom \
  --api-schema file://order-tools-schema.json

# Create an alias for deployment
aws bedrock-agent create-agent-alias \
  --agent-id YOUR-AGENT-ID \
  --agent-alias-name prod-v1

4. Using Strands with Bedrock AgentCore

Strands SDK can also work directly with Bedrock:

javascript

import { BedrockAgentRuntime } from '@aws-sdk/client-bedrock-agent-runtime';
import { StrandsBedrockAdapter } from '@strands/bedrock-adapter';

const bedrockClient = new BedrockAgentRuntime({
  region: 'us-east-1',
  credentials: {
    accessKeyId: process.env.AWS_ACCESS_KEY_ID,
    secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY
  }
});

const adapter = new StrandsBedrockAdapter({
  client: bedrockClient,
  agentId: 'YOUR-AGENT-ID',
  agentAliasId: 'YOUR-ALIAS-ID'
});

const response = await adapter.invoke({
  inputText: "Where's my order ORD-12345?",
  sessionId: 'user-123',
  enableTrace: true
});

console.log(response.completion);

---

Step 6: Connecting to External Knowledge Sources

For production agents, you'll want to connect to knowledge bases for RAG (Retrieval-Augmented Generation).

Using Amazon Bedrock Knowledge Bases:

javascript

// knowledge-base-setup.js
import { BedrockAgent } from '@aws-sdk/client-bedrock-agent';

const bedrockAgent = new BedrockAgent({ region: 'us-east-1' });

// Create a knowledge base from your data sources
const kb = await bedrockAgent.createKnowledgeBase({
  name: 'product-catalog-kb',
  description: 'Product catalog and documentation',
  roleArn: 'arn:aws:iam::123456789012:role/bedrock-kb-role',
  knowledgeBaseConfiguration: {
    type: 'VECTOR',
    vectorKnowledgeBaseConfiguration: {
      embeddingModelArn: 'arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0'
    }
  },
  storageConfiguration: {
    type: 'OPENSEARCH_SERVERLESS',
    opensearchServerlessConfiguration: {
      collectionArn: 'arn:aws:aoss:us-east-1:123456789012:collection/your-collection',
      vectorIndexName: 'bedrock-knowledge-base-index',
      fieldMapping: {
        metadataField: 'metadata',
        textField: 'text'
      }
    }
  }
});

// Sync your data
await bedrockAgent.startIngestionJob({
  knowledgeBaseId: kb.knowledgeBaseId,
  dataSourceId: 'your-data-source-id'
});

Then attach to your agent:

javascript

await bedrockAgent.associateAgentKnowledgeBase({
  agentId: 'YOUR-AGENT-ID',
  knowledgeBaseId: kb.knowledgeBaseId,
  description: 'Product catalog knowledge base'
});

---

Step 7: Testing and Debugging with Kiro

Kiro provides powerful debugging tools for your agent:

1. Run the Agent in Kiro's Sandbox

• Open your agent in Kiro

• Click "Test in Sandbox"

• Enter test queries and watch step-by-step execution

• See exactly which tools were called and why

2. Trace Execution
Kiro's tracing feature shows:

• LLM reasoning steps

• Tool selection process

• Tool execution results

• Final response generation

3. Performance Monitoring

• Token usage per query

• Response latency

• Tool success rates

• Cost estimates

---

Step 8: Advanced Patterns

Pattern 1: Multi-Agent Collaboration

javascript

import { Agent, Runner, handoff } from '@strands/agents-sdk';

const orderAgent = new Agent({
  name: 'Order Agent',
  instructions: 'Handle all order-related queries',
  tools: [checkOrderStatus, initiateReturn]
});

const productAgent = new Agent({
  name: 'Product Agent',
  instructions: 'Handle product inquiries',
  tools: [queryProducts]
});

const triageAgent = new Agent({
  name: 'Triage Agent',
  instructions: 'Route queries to appropriate specialized agents',
  tools: [
    handoff({ agent: orderAgent, description: 'For order-related questions' }),
    handoff({ agent: productAgent, description: 'For product-related questions' })
  ]
});

Pattern 2: Memory and Context

javascript

import { MemoryStore } from '@strands/agents-sdk';

const memory = new MemoryStore({
  type: 'redis',
  url: process.env.REDIS_URL,
  ttl: 3600 // 1 hour
});

const agentWithMemory = new Agent({
  name: 'Agent with Memory',
  instructions: 'Remember user preferences and past conversations',
  memory,
  tools: [...]
});

---

Production Considerations

Aspect	Consideration	Tool/Solution
Scalability	Handle thousands of concurrent users	Bedrock AgentCore auto-scaling
Latency	Keep responses under 2 seconds	Strands streaming, Edge deployment
Cost	Optimize token usage	Kiro monitoring, Bedrock cost controls
Security	Protect sensitive data	Bedrock IAM, VPC, encryption
Observability	Monitor agent behavior	CloudWatch, Kiro analytics
Versioning	Manage agent updates	Bedrock agent aliases, CI/CD
Fallback	Handle LLM failures	Strands retry logic, fallback models

---

Complete Example: Customer Support Agent

Here's a complete, production-ready example combining everything:

javascript

// index.js - Complete Customer Support Agent
import express from 'express';
import { Agent, Runner, MemoryStore } from '@strands/agents-sdk';
import { BedrockRuntimeClient } from '@aws-sdk/client-bedrock-runtime';
import { 
  checkOrderStatus, 
  initiateReturn,
  queryProducts,
  getWeather,
  trackShipment
} from './tools/index.js';
import { connectDatabase } from './database.js';

const app = express();
app.use(express.json());

// Initialize database
await connectDatabase();

// Create memory store
const memory = new MemoryStore({
  type: 'redis',
  url: process.env.REDIS_URL
});

// Create the agent
const agent = new Agent({
  name: 'Ecommerce Support Agent',
  instructions: `
    You are a customer support agent for an online store.
    
    TOOLS AVAILABLE:
    - check_order_status: Check order status
    - initiate_return: Process returns
    - query_products: Search products
    - get_weather: Check weather for delivery
    - track_shipment: Get real-time tracking
    
    GUIDELINES:
    1. Always verify email before sharing order details
    2. Be empathetic with returns
    3. Suggest alternatives if products are out of stock
    4. Ask clarifying questions when needed
    5. End with a clear summary of actions taken
  `,
  tools: [
    checkOrderStatus,
    initiateReturn,
    queryProducts,
    getWeather,
    trackShipment
  ],
  memory,
  model: 'gpt-4-turbo',
  temperature: 0.2
});

// Create runner
const runner = new Runner({ agent, stream: true });

// API endpoint
app.post('/api/chat', async (req, res) => {
  const { message, sessionId } = req.body;
  
  try {
    const response = await runner.run({
      input: message,
      sessionId,
      onToolCall: (toolName, params) => {
        console.log(`Tool called: ${toolName}`, params);
      }
    });
    
    res.json({
      response: response.output,
      toolCalls: response.toolCalls,
      metrics: response.metrics
    });
  } catch (error) {
    console.error('Agent error:', error);
    res.status(500).json({ error: 'Failed to process request' });
  }
});

// Start server
app.listen(3000, () => {
  console.log('Agent API running on port 3000');
});

---

Conclusion

Building production-ready AI agents is now accessible to every developer thanks to modern tools like Kiro, Strands SDK, and Amazon Bedrock AgentCore.

Key Takeaways:

• Kiro provides an intuitive visual environment for designing and testing agent workflows

• Strands SDK simplifies tool definition, agent orchestration, and memory management

• Bedrock AgentCore handles production deployment, scaling, and security

• External data sources (databases, APIs, knowledge bases) make agents truly useful

• Modern tooling abstracts away complexity so you can focus on agent behavior

Your Agent Development Roadmap:

1. Start Simple – Build a basic agent with 2-3 tools in Kiro

2. Add Real Data – Connect to your actual databases and APIs

3. Test Thoroughly – Use Kiro's sandbox for edge cases

4. Deploy to Bedrock – Scale with AWS managed services

5. Monitor & Improve – Use analytics to refine agent behavior

---

Resources

• Kiro Official Documentation

• Strands Agents SDK GitHub

• Amazon Bedrock AgentCore Documentation

• Sample GitHub Repository (Link to complete example code)

• AWS Bedrock Pricing

• Strands Discord Community