The "DevOps Squad" (Sandboxed AI Environments)

The Design Challenge: Safety. How to architect a system where AI writes and executes code without destroying your production environment or leaking credentials.
Recommended Stack: Docker Containers for ephemeral Sandboxed AI Environments.
Global Use Case: Designing an open-source alternative to "Devin" for automated bug patching.
Author: AgileWoW Team
Category: AI DevOps / Security
Read Time: 12 Minutes
Parent Guide: The Agentic AI Engineering Handbook

1. The Design Challenge: The "Rogue Agent" Risk

Most AI demos run code directly on the user's laptop (exec()). In a production DevOps pipeline, this is unacceptable.

The Risk Vector:

• Dependency Pollution: Agent installs a conflicting version of numpy, breaking other apps.
• Credential Leakage: Agent accidentally prints ENV variables to the logs.
• Destructive Commands: Agent tries to delete a "temporary" directory that turns out to be /etc.

The Solution: Ephemeral Sandboxing. Every task gets a fresh, isolated container that is destroyed immediately after execution.

2. The Tech Stack Selection

We need a way to spin up isolated environments in milliseconds, not minutes.

3. Architecture Deep Dive: The "Coding Agent" Loop

3.1 The Agent Roster

We separate the "Planner" from the "Executor" to ensure safety.

1. The Architect (Planner):
   • Role: Reads the GitHub Issue. Plans the fix.
   • Tools: read_file, search_code.
   • Constraint: Cannot execute code. Only proposes changes.
2. The Builder (Executor):
   • Role: Writes the code and runs the tests.
   • Environment: Lives inside the Docker container.
   • Constraint: No internet access (except strictly whitelisted PyPI mirrors).
3. The Tester (QA):
   • Role: Runs the reproduction script.
   • Verdict: If test fails -> Send stderr back to Builder. If pass -> Create Pull Request.

3.2 The Sandbox Protocol

How do we safely execute code?

• Spin Up: The system starts a python:3.9-slim container with the repo mounted as Read-Only (initially).
• Copy: We copy the specific file to be modified into a writable /tmp/workspace.
• Execute: The Agent runs python /tmp/workspace/fix.py.
• Tear Down: Once the result is returned, the container is killed. No state persists.

4. Implementation Guide (Docker SDK)

Phase 1: The Docker Controller

We create a Python wrapper to manage the lifecycle of these ephemeral containers.

import docker
import tarfile
import io

client = docker.from_env()

def run_in_sandbox(code: str, image="python:3.9-slim"):
    # 1. Start the container (detached)
    container = client.containers.run(
        image, 
        command="tail -f /dev/null", # Keep it alive
        detach=True,
        network_mode="none" # CRITICAL: No Internet
    )
    
    try:
        # 2. Inject the code
        exec_result = container.exec_run(f"python -c '{code}'")
        return exec_result.output.decode("utf-8")
    finally:
        # 3. Kill it with fire
        container.remove(force=True)

Phase 2: The Feedback Loop

The most important part of a "Devin" alternative is the ability to read errors.

# Agent Prompt
"""
You tried to run the code, but it failed with:
{stderr}

Analyze the error. Rewrite the code to fix the 'IndexError'.
"""

5. Global Use Case: The "Auto-Patcher"

Imagine a "GitHub Bot" that doesn't just label issues, but fixes them.

• Trigger: A user opens an issue: "Bug: Division by zero in calc.py when input is empty."
• Action:
  • The Architect reads calc.py.
  • The Builder writes a test case that reproduces the crash (DivisionByZeroError).
  • The Builder modifies calc.py to add if input: ....
  • The Tester runs the test case. It passes.
  • The System opens a PR: "Fix: Handle empty input in calculator."

6. Frequently Asked Questions (FAQ)

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