If you’re building AI agents for investment banking, you need two things: orchestration and isolation. AutoGen gives you the multi-agent workflow layer, while Docker gives you reproducible, sandboxed execution for anything that touches market data, model calls, or internal tooling.

That combination is useful when you want analysts, risk checks, and compliance review to run as separate agents, but still execute code in a controlled container with pinned dependencies and no host contamination.

Prerequisites

• •Python 3.10+
• •Docker Engine installed and running
• •Access to an OpenAI-compatible model endpoint or Azure OpenAI if your AutoGen setup uses it
• •autogen-agentchat installed
• •docker Python SDK installed
• •Basic familiarity with:
  • •autogen_agentchat
  • •docker.from_env()
  • •running containers with bind mounts

Install the packages:

pip install autogen-agentchat autogen-ext[docker] docker

Integration Steps

1. •

   Create a Docker-backed execution environment

   For investment banking workflows, don’t run agent-generated code on the host. Use Docker to execute valuation scripts, data transforms, or report generation in a container.

   import docker
   
   client = docker.from_env()
   
   container = client.containers.run(
       image="python:3.11-slim",
       command="sleep 300",
       detach=True,
       tty=True,
       name="ib-agent-sandbox",
   )
   
   print(container.id)
   

   This gives you a clean runtime where your agent can execute code without polluting the machine running AutoGen.

2. •

   Define an AutoGen assistant agent for investment banking tasks

   Use an assistant agent to handle tasks like comparable company analysis, earnings summary extraction, or draft memo generation.

   from autogen_agentchat.agents import AssistantAgent
   
   analyst = AssistantAgent(
       name="investment_banking_analyst",
       model_client=None,  # plug in your configured model client here
       system_message=(
           "You are an investment banking analyst. "
           "Produce concise outputs suitable for IC memos and valuation work."
       ),
   )
   

   In production, replace model_client=None with your actual model client configuration. The important part is that the agent is now isolated from execution concerns.

3. •

   Run a Dockerized computation from the agent workflow

   A common pattern is: the agent decides what to compute, then your orchestration layer sends that computation into Docker.

   import docker
   from pathlib import Path
   
   client = docker.from_env()
   
   project_dir = Path.cwd()
   script_path = project_dir / "valuation.py"
   
   script_path.write_text("""
   

import json

revenue = 12000000 ebitda = 3400000 multiple = 12.5

ev = ebitda * multiple print(json.dumps({"enterprise_value": ev})) """)

result = client.containers.run( image="python:3.11-slim", command=["python", "/workspace/valuation.py"], volumes={ str(project_dir): {"bind": "/workspace", "mode": "rw"} }, working_dir="/workspace", remove=True, )

print(result.decode())

This pattern is what you want for controlled financial calculations, file parsing, or report generation.

4. **Wire the agent output into the container execution path**

AutoGen handles reasoning; Docker handles execution. The integration point is usually a small tool function that takes structured instructions from the agent and runs them in a container.

```python
 import json
 import docker

 client = docker.from_env()

 def run_finance_job(payload: dict) -> str:
     code = f"""
import json

revenue = {payload["revenue"]}
ebitda = {payload["ebitda"]}
multiple = {payload["multiple"]}

enterprise_value = ebitda * multiple
output = {{
 "revenue": revenue,
 "ebitda": ebitda,
 "multiple": multiple,
 "enterprise_value": enterprise_value
}}
print(json.dumps(output))
"""
     return client.containers.run(
         image="python:3.11-slim",
         command=["python", "-c", code],
         remove=True,
     ).decode()

 payload = {"revenue": 12000000, "ebitda": 3400000, "multiple": 12.5}
 print(run_finance_job(payload))

In a real system, the agent would produce payload after parsing a pitch deck or answering an analyst prompt.

5. •

   Use AutoGen to coordinate multiple roles around the same containerized job

   Investment banking workflows usually need more than one perspective: analyst, reviewer, and compliance checker.

   from autogen_agentchat.agents import AssistantAgent
   
   analyst = AssistantAgent(name="analyst", model_client=None)
   reviewer = AssistantAgent(name="reviewer", model_client=None)
   
   task = """
   

Draft a valuation summary for a software company. Assume EBITDA of $3.4M and exit multiple of 12.5x. """

Pseudocode for the orchestration layer:

1) analyst produces structured inputs

2) Docker runs the calculation

3) reviewer validates the output and flags issues

## Testing the Integration

Use a minimal end-to-end test: generate inputs with Python, run them in Docker, and confirm the output matches expectations.

```python
import docker
import json

client = docker.from_env()

code = """
import json
ebitda = 3400000
multiple = 12.5
print(json.dumps({"enterprise_value": ebitda * multiple}))
"""

output = client.containers.run(
 image="python:3.11-slim",
 command=["python", "-c", code],
 remove=True,
)

data = json.loads(output.decode())
assert data["enterprise_value"] == 42500000.0

print("Integration OK:", data)

Expected output:

Integration OK: {'enterprise_value': 42500000.0}

If this passes, Docker execution works and your orchestration layer can safely hand off finance computations from AutoGen-driven agents.

Real-World Use Cases

• •

  Pitch book automation

  • •One agent extracts company metrics from source docs.
  • •Docker runs valuation math and chart generation.
  • •Another agent drafts the management summary.
• •

  Due diligence assistants

  • •Agents classify risks across contracts, financial statements, and KPI packs.
  • •Containerized jobs run OCR, parsing, or spreadsheet normalization with pinned dependencies.
• •

  Compliance-aware research workflows

  • •An analyst agent proposes outputs.
  • •A reviewer agent checks tone and policy constraints.
  • •Docker executes only approved transformations on internal datasets.

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.