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.
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