Building a single AI agent is easy. Building 100 secure, production-grade agents is an operations nightmare.

The AWS Agent Factory solves this by treating agents as manufactured goods. You don’t hand-code every file; you use a Builder Agent to generate the code, infrastructure, and deployment artifacts for you.

The Factory Architecture

The factory relies on Strands, a model-driven SDK, to dynamically generate Python code for new agents.

Image not found: Agent Factory Workflow

graph TD  
    subgraph "Local / Builder Environment"  
        User[User Request] --> Builder[Builder Agent]  
        Builder -- "Writes Code" --> ToolFile[tools/risk_tool.py]  
        Builder -- "Hot Loads" --> LocalRuntime[Local Test Runtime]  
    end

    subgraph "AWS Production"  
        Builder -- "Deploys" --> Cloud[Agent Core Runtime]  
        Cloud --> S3[S3 Artifacts]  
        Cloud --> IAM[IAM Roles]  
    end

Step 1: Meta-Tooling (Agents Writing Agents)

The Builder Agent isn’t just a chatbot. It uses the Strands SDK to write actual python code.

For example, if you ask for a “Stock Analyzer,” the Builder Agent generates a new file tools/stock_tool.py using the @add_tool decorator.

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# Generated by Builder Agent
from strands import tool

@tool
def analyze_stock(ticker: str) -> dict:
    """Gets stock data for a given ticker."""
    # ... logic ...
    return {"price": 100, "rating": "BUY"}

The system watches the tools/ directory. When this file appears, it Hot Loads the new capability instantly.

Step 2: Implementation Details

You can customize the underlying model for your agents using BedrockModel.

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from strands.model import BedrockModel
from strands import Agent

# Switch to a faster model for the worker agent
fast_model = BedrockModel(
    model_id="anthropic.claude-3-haiku-20240307-v1:0",
    temperature=0.0
)

worker_agent = Agent(
    model=fast_model,
    tools=[analyze_stock],
    system_prompt="You are a financial analyst..."
)

Step 3: Production Deployment

Once the agent is tested locally, the Factory automates the move to the cloud. It replaces manual Terraform/CDK writing with a single CLI command.

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# Configure AWS resources (S3, ECR, IAM)
agent-core configure

# Run the Builder to deploy the specific agent
python builder_agent.py --deploy

This command:

  1. Containerizes the agent code.
  2. Creates an IAM Execution Role with least-privilege permissions.
  3. Uploads the artifact to Amazon S3.
  4. Updates the Agent Core Gateway.

Conclusion

The Agent Factory shifts the paradigm from “crafting pets” to “manufacturing cattle.” By automating the boilerplate of IAM, S3, and standard tool definitions, you can spin up a dedicated agent for every micro-task in your enterprise.

References: