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Monday, 12 October 2026

Teach2Learn AI Agents: The Future of Personalised Learning

What Are Teach2Learn AI Agents?

Imagine a teacher who is always available, never judges you, and makes you feel smarter with every conversation. That is exactly what Teach2Learn AI agents do. They flip the traditional classroom upside down. Instead of the AI teaching you, you teach the AI.

Research in educational technology has shown that this "learning by teaching" approach helps students understand concepts more deeply

. When you explain something to someone else, your brain processes the information differently. You organise your thoughts, find gaps in your understanding, and build stronger memory connections.

The Teach2Learn system uses a team of specialised AI agents working together to create this experience. Think of them like a group of friendly helpers, each with a specific job.

How Does Teach2Learn Actually Work?

The system follows a simple workflow with five AI agents, each playing a unique role.

1. Question-Pool Generator Agent

This agent creates the questions you will discuss. It uses Bloom's Taxonomy - a proven educational framework - to generate questions at different difficulty levels. Some questions check basic recall, while others push you to analyse and evaluate.

2. Student Agent (The LbT Bot)

This is the heart of the system. The Student Agent acts like a novice learner who genuinely wants to understand the topic you are teaching. It does not pretend to know everything.

Here is a real example from the Teach2Learn architecture:

Good Student Agent Response: "I understood why a photon has no rest mass, but I'm still confused about wave-particle duality. Could you explain that?"

Bad AI Response: "A photon has no rest mass because..." (This is bad because the AI starts teaching you instead of asking for clarification.)

The Student Agent should ask questions, express confusion, and seek clarification - just like a real student would.

3. Evaluator Agent

While you teach, this agent performs qualitative evaluation of the conversation. It looks at how well you explained concepts, whether you used examples, and if you addressed the Student Agent's confusion.

4. Scorer Agent

This agent handles quantitative evaluation. It assigns scores based on predefined criteria, giving you measurable feedback on your teaching performance.

5. Summary Agent

At the end, this agent creates a final summary of the entire session. It highlights what you taught well and identifies areas where you could improve.

Why Teach2Learn Works Better Than Traditional AI Tutors

Recent research from IEEE Xplore describes an AI-powered adaptive tutoring platform that includes a "Feynman Board" - a feature that lets students explain ideas back to the system to enhance understanding

. This research confirms what Teach2Learn already implements: explaining concepts strengthens learning.

Traditional AI tutors simply answer questions. Teach2Learn AI agents make you the expert. When you have to explain something clearly, you cannot hide behind vague understanding. The Student Agent will ask follow-up questions that expose weak spots in your knowledge.

Real-World Impact: From MHT CET to Everyday Learning

Consider a student preparing for the MHT CET (Maharashtra Health and Technical Common Entrance Test). Instead of passively watching videos or reading notes, the student could:

Select a topic like "Laws of Motion" or "Chemical Bonding"
Generate questions through the Question-Pool Generator Agent
Teach the Student Agent about the topic
Receive detailed feedback from the Evaluator and Scorer Agents
Get a summary of strengths and weaknesses

The MHT CET itself uses a comprehensive syllabus covering Physics, Chemistry, and Mathematics or Biology

. This makes it perfect for the Teach2Learn approach, where deep conceptual understanding matters more than memorisation.

The Technical Magic Behind Teach2Learn

For those curious about the technology, systems like Teach2Learn use a multi-agent architecture. Each agent is powered by large language models (LLMs) but with different prompts and goals.

Open-source projects like alai demonstrate similar approaches, where a tutoring AI logs every mistake, hint, and successful explanation to build a learner profile

. This profile then shapes future sessions, making learning truly personalised.

The key technical components include:

Natural Language Understanding to follow your explanations
Knowledge Tracking to identify gaps in understanding
Adaptive Questioning to probe deeper into concepts
Session Memory to remember your progress over time

Getting Started with Learning by Teaching

You do not need special software to benefit from this approach. Here are simple ways to apply Teach2Learn principles:

Teach a friend (or even a pet) about what you learned today
Write explanations in your own words as if explaining to a beginner
Ask yourself "If I were a student, what would confuse me?"
Use AI chatbots and tell them to act like a novice student

The next time you study, try this: instead of reading and re-reading, close the book and explain the concept out loud. You will quickly discover what you truly understand and what needs more work.

The Future of AI-Powered Education

Teach2Learn represents a fundamental shift in educational technology. Rather than replacing teachers, it makes every learner a teacher. This builds confidence, deepens understanding, and develops communication skills that matter in every profession.

As AI systems become more sophisticated, we can expect Teach2Learn agents to understand subjects more deeply, ask more insightful questions, and provide even better feedback. The future of learning is not about AI teaching humans - it is about humans and AI learning together.

Tuesday, 10 March 2026

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:

# 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:

# Using npm
npm install @strands/agents-sdk

# Using pip
pip install strands-agents-sdk

Define Tools (JavaScript/TypeScript):

// 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:

# 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)

// 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

// 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.

// 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:

// 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

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

2. Package Your Agent for Bedrock

Create a deployment configuration:

// 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

# 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:

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:

// 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:

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

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

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:

// 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:

Start Simple – Build a basic agent with 2-3 tools in Kiro
Add Real Data – Connect to your actual databases and APIs
Test Thoroughly – Use Kiro's sandbox for edge cases
Deploy to Bedrock – Scale with AWS managed services
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

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