AI Agent Swarm: Fixing GitHub Issues Autonomously

Zero human intervention is actually possible when you stop treating AI as a chat interface and start treating it as a pipeline of specialized roles. I built a system where I can open a GitHub issue and walk away, only to return to a completed pull request that has already passed tests and a peer review.

The Agent Architecture

Instead of one "do-it-all" bot, I split the workflow across four distinct agents to prevent hallucinations and maintain a strict quality gate.

• The Architect: Analyzes the repo and codebase structure to create a concrete implementation plan. It posts this plan as a GitHub comment for visibility.
• The Coder: Operates in a cloud sandbox. It clones the repo, implements the plan, runs the linter, and executes the test suite. It has a three-strike limit to fix its own bugs based on real compiler/test errors.
• The Reviewer: Performs a line-by-line diff analysis. It specifically hunts for hardcoded secrets, SQL injection risks, and poor error handling. It either issues an APPROVED status or sends the code back for rework.
• The PR Agent: Handles the final documentation, drafting the PR with a summary of changes and the actual test output.

Technical Implementation & Workflow

The key to making this a real-world AI workflow is the removal of LLM-based routing. I used pure Python if/else logic for the supervisor to ensure the control flow is predictable and cannot loop infinitely.

For the infrastructure, I relied on a specific stack to keep costs at zero:

• LLM: Llama 3.1 via Groq (high throughput, free tier).
• Runtime: E2B for secure, isolated Ubuntu sandboxes.
• Orchestration: LangGraph for the agent state machine.
• Backend/Frontend: FastAPI and Next.js with WebSockets for real-time log streaming.

Lessons from the Deep Dive

The biggest hurdle wasn't the prompt engineering, but the environment. Most "autonomous" demos are simulations; here, the Coder is interacting with a real shell. If a test fails, it's a real failure.

One major gotcha: system prompt synchronization. I once had a model attempt to call a non-existent tool eleven times in a row simply because the tool was described in the prompt but not actually implemented in the code.

This setup proves that a multi-agent system with isolated contexts and hard-coded routing is far more reliable than a single agent with a massive toolset.