How to Integrate AutoGen for pension funds with Docker for startups

AutoGen is useful when you need multi-agent reasoning around pension workflows: document intake, eligibility checks, contribution calculations, and member communications. Docker gives you the missing piece for startups — a repeatable runtime so those agents behave the same on a laptop, in CI, and in production.

Combine them and you get an agent system that can run pension operations logic in isolated containers, scale per workflow, and keep dependencies from leaking across services.

Prerequisites

• •Python 3.10+
• •Docker Engine installed and running
• •A working AutoGen package installed in your project
• •Access to your pension fund data sources or sandbox APIs
• •Basic familiarity with Python async code
• •A .env file or secret manager for API keys if your agents call external models

Install the core libraries:

pip install pyautogen docker python-dotenv

Integration Steps

1. •

   Create a Dockerized runtime for the agent service

   Start by defining a container that runs your AutoGen orchestration code. This keeps your pension workflow isolated from local machine differences.

FROM python:3.11-slim

WORKDIR /app

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

COPY . .

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

A minimal requirements.txt:

pyautogen
docker
python-dotenv

2. •

   Define your AutoGen agents for pension workflows

   Use AssistantAgent for reasoning and UserProxyAgent for controlled execution. In a pension context, one agent can classify incoming requests while another validates calculations.

import os
from autogen import AssistantAgent, UserProxyAgent

llm_config = {
    "model": os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
    "api_key": os.getenv("OPENAI_API_KEY"),
}

pension_analyst = AssistantAgent(
    name="pension_analyst",
    llm_config=llm_config,
    system_message=(
        "You analyze pension fund requests. "
        "Extract member intent, identify required documents, "
        "and flag compliance risks."
    ),
)

executor = UserProxyAgent(
    name="executor",
    human_input_mode="NEVER",
    code_execution_config=False,
)

This setup is enough for structured conversation flow. In production, keep the executor locked down and only allow explicit tool calls.

3. •

   Use the Docker SDK to run isolated processing containers

   The Docker SDK lets you spin up per-request containers for document parsing or calculation jobs. That is useful when a pension workflow needs deterministic dependencies like PDF parsers or actuarial libraries.

import docker

client = docker.from_env()

container = client.containers.run(
    image="python:3.11-slim",
    command="python -c 'print(\"pension job started\")'",
    detach=True,
    remove=True,
)

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

For a startup, this pattern is practical because each request gets its own sandbox. You avoid one agent run polluting another with cached state or conflicting package versions.

4. •

   Connect AutoGen conversation output to Docker execution

   Let the AutoGen agent produce structured instructions, then pass those instructions into a containerized worker. The key is to keep the LLM responsible for planning and Docker responsible for execution.

import json
import docker
from autogen import AssistantAgent, UserProxyAgent

client = docker.from_env()

planner = AssistantAgent(
    name="planner",
    llm_config={"model": "gpt-4o-mini", "api_key": os.getenv("OPENAI_API_KEY")},
)

user = UserProxyAgent(name="user", human_input_mode="NEVER")

message = """
Create a JSON plan for validating a pension withdrawal request.
Include fields: member_id, action, required_docs.
"""

plan_response = planner.generate_reply(messages=[{"role": "user", "content": message}])
print(plan_response)

plan = {
    "member_id": "M-10422",
    "action": "validate_withdrawal",
    "required_docs": ["id_document", "bank_statement", "withdrawal_form"],
}

container = client.containers.run(
    image="python:3.11-slim",
    command=[
        "python",
        "-c",
        f"import json; print(json.dumps({json.dumps(plan)}, indent=2))"
    ],
    detach=True,
    remove=True,
)

print(container.logs().decode())

In real systems, replace the inline command with a mounted script that performs validation against internal policy rules.

5. •

   Wire the whole flow into one service entrypoint

   Your service should accept a request, ask AutoGen to classify it, then dispatch work into Docker based on the result.

import os
import docker
from autogen import AssistantAgent

client = docker.from_env()

agent = AssistantAgent(
    name="pension_agent",
    llm_config={
        "model": os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
        "api_key": os.getenv("OPENAI_API_KEY"),
    },
)

def handle_request(text: str):
    reply = agent.generate_reply(messages=[{"role": "user", "content": text}])

    container = client.containers.run(
        image="python:3.11-slim",
        command=f"python -c 'print(\"processed request\")'",
        detach=True,
        remove=True,
    )

    return {
        "agent_reply": reply,
        "container_id": container.id,
        "status": container.status if hasattr(container, "status") else "started",
    }

if __name__ == "__main__":
    result = handle_request("Validate pension withdrawal request for member M-10422.")
    print(result)

Testing the Integration

Run a simple smoke test that confirms both parts are working: AutoGen can produce a response and Docker can execute a containerized task.

import os
import docker
from autogen import AssistantAgent

client = docker.from_env()

agent = AssistantAgent(
    name="test_agent",
    llm_config={
        "model": os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
        "api_key": os.getenv("OPENAI_API_KEY"),
    },
)

reply = agent.generate_reply(
    messages=[{"role": "user", "content": "Return 'ok' if you can classify a pension request."}]
)

container = client.containers.run(
    image="python:3.11-slim",
    command="python -c 'print(\"docker ok\")'",
    detach=True,
    remove=True,
)

print("AutoGen reply:", reply)
print("Docker logs:", container.logs().decode())

Expected output:

AutoGen reply: ok
Docker logs: docker ok

If you get both outputs, your orchestration path is working end to end.

Real-World Use Cases

• •

  Pension document triage

  • •AutoGen classifies incoming emails or uploaded forms.
  • •Docker runs PDF extraction, OCR, or policy validation in isolated workers.
• •

  Contribution reconciliation

  • •An agent compares payroll inputs against fund rules.
  • •Containerized jobs calculate deltas using pinned financial libraries.
• •

  Member support automation

  • •One agent drafts responses for benefit queries.
  • •Another container checks eligibility logic before anything is sent to operations staff.

This setup is strong for startups because it separates reasoning from execution. AutoGen handles coordination; Docker gives you repeatable infrastructure that won’t collapse when your first real pension workflow hits production load.

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.