Building AI Agents with Claude on AWS Bedrock: The Easy Way

TL;DR: Building AI agents doesn't have to be complex. With Claude and AWS Bedrock, you can create intelligent agents in minutes. I've open-sourced a framework to make it even easier.

The Problem

AI agents are everywhere now. They're powerful. They're flexible. But they're also... complicated.
Here's what you typically need to do:

• Set up an LLM API client
• Build tool definitions (function schemas)
• Implement a loop that handles tool-use responses
• Manage conversation memory
• Handle errors and retries
• Figure out streaming responses That's a lot of boilerplate before you even get to the interesting part: defining what your agent should do. And if you're using Claude on AWS Bedrock? You're also managing AWS credentials, IAM roles, and regional endpoints. What if there was a simpler way? --- ## The Solution I built Claude AWS Bedrock Agents Framework — an open-source Python package that removes all that friction. With just a few lines of code, you get:
• ✅ Simple Agent API (define tools, run agents)
• ✅ Built-in conversation memory
• ✅ Streaming support for long-running tasks
• ✅ Tool-use handled automatically
• ✅ AWS Bedrock integration out of the box
• ✅ Clean error handling Let me show you how easy it is. --- ## Getting Started (5 Minutes) ### Installation

bash
pip install claude-bedrock-agents
Your First Agent
Create a file called math_agent.py:

from claude_bedrock_agents import Agent, Tool

# Step 1: Define tools (functions your agent can use)
def add_numbers(a: int, b: int) -> int:
    """Add two numbers together"""
    return a + b

def multiply_numbers(a: int, b: int) -> int:
    """Multiply two numbers"""
    return a * b

# Step 2: Create the agent
agent = Agent(
    model="claude-3-sonnet-20240229",
    tools=[
        Tool(add_numbers),
        Tool(multiply_numbers)
    ],
    system_prompt="You are a helpful math assistant. Use the provided tools to solve math problems."
)

# Step 3: Run it!
response = agent.run("What is 10 + 5?")
print(response)

# Continue the conversation
response = agent.run("Now multiply that result by 3")
print(response)
Run It
python math_agent.py
Output:

The sum of 10 + 5 is 15.
Then:

15 × 3 = 45
That's it. Your first AI agent is working.

Real-World Examples
1️⃣ Data Analysis Agent
from claude_bedrock_agents import Agent, Tool
import json

def query_database(sql: str) -> str:
    """Execute a SQL query on your data warehouse"""
    # Your actual database query logic here
    return json.dumps({
        "rows": 1000,
        "columns": ["date", "revenue", "customers"],
        "data": [...]
    })

def generate_report(data: str) -> str:
    """Generate insights from data"""
    return "Report: Q3 saw 20% YoY growth in revenue"

agent = Agent(
    model="claude-3-sonnet-20240229",
    tools=[
        Tool(query_database, description="Query your data warehouse"),
        Tool(generate_report, description="Generate business reports")
    ],
    system_prompt="You are a data analyst. Analyze queries and generate reports."
)

# Use it
result = agent.run("Analyze Q3 sales data and create a summary report")
print(result)
2️⃣ Customer Support Agent
from claude_bedrock_agents import Agent, Tool, Memory

def search_knowledge_base(query: str) -> str:
    """Search support documentation"""
    return "Found 5 articles on account recovery..."

def create_support_ticket(issue: str) -> str:
    """Create a support ticket"""
    return "Ticket #12345 created"

# Use memory to remember conversation context
memory = Memory(max_messages=50)

agent = Agent(
    model="claude-3-sonnet-20240229",
    tools=[
        Tool(search_knowledge_base),
        Tool(create_support_ticket)
    ],
    memory=memory,
    system_prompt="You are a helpful customer support agent."
)

# Multi-turn conversation
agent.run("I can't login to my account")
agent.run("I forgot my password")  # Agent remembers the context!
agent.run("Create a ticket for me")
3️⃣ AWS Infrastructure Agent
from claude_bedrock_agents import Agent, Tool

def list_s3_buckets() -> str:
    """List all S3 buckets in your AWS account"""
    return "Buckets: analytics-raw, analytics-processed, backups"

def describe_lambda_functions() -> str:
    """List Lambda functions"""
    return "Functions: data-ingestion, transformation, cleanup"

def get_cost_analysis(days: int) -> str:
    """Get AWS cost analysis"""
    return "Last 30 days: $1,234 (S3: $600, Lambda: $400, RDS: $234)"

agent = Agent(
    model="claude-3-sonnet-20240229",
    tools=[
        Tool(list_s3_buckets),
        Tool(describe_lambda_functions),
        Tool(get_cost_analysis)
    ],
    system_prompt="You are an AWS infrastructure expert."
)

# Ask your infrastructure questions naturally
result = agent.run(
    "What S3 buckets do we have and what's our estimated monthly AWS bill?"
)
print(result)
Why This Matters
For Developers
Less boilerplate = more focus on business logic
AWS Bedrock handles authentication
Tool system is intuitive and Pythonic
For Teams
Reproducible AI agents (no more prompt engineering guesswork)
Easy to share (one pip install away)
Memory management out of the box
For Production
Streaming for real-time responses
Error handling built-in
Logging for debugging
How It Works Under the Hood
The framework handles the complex part automatically. You write the simple part. The framework handles the complexity.

Advanced Features
Streaming Responses
agent = Agent(model="claude-3-sonnet-20240229")

# Stream responses in real-time
for chunk in agent.stream("Analyze this large dataset"):
    print(chunk, end="", flush=True)
Custom Memory Backend
class DatabaseMemory(Memory):
    def __init__(self, db_connection):
        self.db = db_connection

    def add_message(self, role: str, content: str):
        self.db.insert("messages", {"role": role, "content": content})

    def get_messages(self):
        return self.db.query("SELECT * FROM messages")

agent = Agent(
    model="claude-3-sonnet-20240229",
    memory=DatabaseMemory(your_db_connection)
)
Get Started Now
📦 Installation
pip install claude-bedrock-agents
📚 Full Documentation
GitHub: claude-bedrock-agents

💬 Questions?
Open an issue on GitHub or comment below!

What's Next?
If you found this useful:

✭ Star the repo on GitHub to help others discover it
💬 Comment below with your use case
🔗 Share with teammates who build with AI

I'm also working on two other open-source tools:

Power BI Data Engineer's Toolkit — Automate Power BI like a pro
AWS Serverless Analytics Pipeline — Deploy complete data pipelines in minutes
See you in the next post! 🚀

Have you built AI agents before? What was your biggest pain point? Let me know in the comments!