How to Integrate AutoGen for pension funds with Docker for production AI

Combining AutoGen for pension funds with Docker gives you a clean path to run regulated AI workflows in isolated, repeatable environments. For pension operations, that means you can spin up agent pipelines for document review, member query handling, and policy checks without depending on a developer laptop or a fragile local setup.

Prerequisites

• •Python 3.10+
• •Docker Desktop or Docker Engine installed and running
• •pip and a virtual environment tool such as venv
• •Access to the AutoGen package used in your pension fund stack
• •A Docker Hub account if you plan to push images to a registry
• •Basic familiarity with:
  • •Python classes and functions
  • •Dockerfiles
  • •REST or internal service calls between agents

Install the Python packages:

pip install pyautogen docker

Integration Steps

1. •Set up your AutoGen pension fund agent configuration.

Start by defining the agent that will handle pension-specific tasks such as contribution summaries, benefit checks, or compliance review. In AutoGen, the core pattern is still AssistantAgent plus a user-facing proxy or orchestrator.

from autogen import AssistantAgent, UserProxyAgent

llm_config = {
    "model": "gpt-4o-mini",
    "api_key": "YOUR_OPENAI_API_KEY",
    "temperature": 0,
}

pension_agent = AssistantAgent(
    name="pension_policy_agent",
    llm_config=llm_config,
    system_message=(
        "You are a pension operations assistant. "
        "Answer using policy-safe language and flag cases needing human review."
    ),
)

user_proxy = UserProxyAgent(
    name="ops_proxy",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=3,
)

2. •Wrap the agent workflow in a callable function.

This keeps your orchestration logic testable and easy to run inside a container. The function below sends a pension-related prompt into the AutoGen conversation loop.

def run_pension_review(query: str) -> str:
    chat_result = user_proxy.initiate_chat(
        pension_agent,
        message=query,
    )
    return str(chat_result)

if __name__ == "__main__":
    result = run_pension_review(
        "Review this member request: can they access partial retirement benefits at age 58?"
    )
    print(result)

3. •Build a Docker image for the agent runtime.

Docker gives you consistent dependencies and predictable startup behavior across dev, staging, and production. Keep the image small and explicit.

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["python", "app.py"]

Your requirements.txt should include:

pyautogen
docker

4. •Use the Docker SDK from Python to build and run the agent container.

This is where the integration becomes operational. The docker Python SDK lets you build images, start containers, stream logs, and inspect exit codes from inside your control service.

import docker

client = docker.from_env()

image_tag = "pension-autogen-agent:latest"

print("Building image...")
client.images.build(path=".", tag=image_tag)

print("Running container...")
container = client.containers.run(
    image_tag,
    detach=True,
    environment={
        "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY"
    }
)

logs = container.logs(stream=False).decode("utf-8")
print(logs)

exit_code = container.wait()["StatusCode"]
print(f"Container exited with code: {exit_code}")

5. •Combine orchestration and runtime checks for production safety.

In production AI systems, you want deterministic startup checks before routing work to an agent. A practical pattern is: start container, verify health output, then send pension cases only if the container is healthy.

import docker

def start_and_verify():
    client = docker.from_env()
    container = client.containers.run(
        "pension-autogen-agent:latest",
        detach=True,
        environment={"OPENAI_API_KEY": "YOUR_OPENAI_API_KEY"},
        command="python app.py"
    )

    logs = container.logs(stream=False).decode("utf-8")
    if "pension_policy_agent" not in logs:
        raise RuntimeError("AutoGen agent did not initialize correctly")

    return container.id

container_id = start_and_verify()
print(f"Started container: {container_id}")

Testing the Integration

Use a simple smoke test that validates both layers: AutoGen can generate a response, and Docker can launch the same code in isolation.

import docker

client = docker.from_env()

container = client.containers.run(
    "pension-autogen-agent:latest",
    detach=True,
    environment={"OPENAI_API_KEY": "YOUR_OPENAI_API_KEY"},
)

output = container.logs(stream=False).decode("utf-8")
print(output)

assert "Review this member request" in output or len(output) > 0
print("Integration test passed")

Expected output:

Building image...
Running container...
...agent initialized...
...response generated...
Container exited with code: 0
Integration test passed

Real-World Use Cases

• •

  Member benefit triage

  • •Run an AutoGen agent inside Docker to classify incoming pension queries by urgency, policy type, and required escalation path.
• •

  Document review pipelines

  • •Containerize an agent that reads retirement forms, flags missing fields, and routes exceptions to compliance staff.
• •

  Policy Q&A services

  • •Deploy isolated agent workers that answer internal staff questions about contribution rules, vesting schedules, or retirement eligibility.

If you want this ready for production, add structured logging, secrets management via environment variables or vaults, and health checks on the container entrypoint. That gives you an auditable AI service instead of a notebook demo wrapped in a shell script.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.