I'm trying to move past simple "Chat-with-PDF" bots and create a team of AI agents that can actually write, test, and deploy code. I've looked at frameworks like Autogen and CrewAI, but I'm struggling with the orchestration. How do you stop agents from "arguing" or getting stuck in a loop when the "Coder Agent" makes a mistake and the "Reviewer Agent" keeps rejecting it? I need a practical workflow for managing these autonomous interactions without constant human intervention.
3 answers
Managing "Agentic" teams requires a "Manager" node. In frameworks like CrewAI, you can assign a Manager Agent whose only job is to delegate tasks and resolve conflicts. If the Coder and Reviewer are looping, the Manager should have a "cooling off" logic where it intervenes after 3 failed attempts. Another tip is to provide the Reviewer with very specific "Success Criteria" in its prompt. Instead of saying "Review this code," say "Check for these 5 security vulnerabilities." This makes the feedback objective and helps the Coder Agent provide a fixed version much faster.
Are you using the same LLM for both agents? I've found that using a "strong" model like GPT-4 for the Reviewer and a "faster" model like 3.5-Turbo or Claude Haiku for the Coder can lead to better results.
Use "Human-in-the-loop" for the final deployment step. Let the agents do the heavy lifting of writing and testing, but don't let them push to production without a physical "OK" click.
Safety first, Melissa! Especially in software development, an autonomous agent could accidentally overwrite a config file. That human gate is a necessary sanity check for any AI team.
That is a brilliant strategy, Steven. It’s like having a senior architect oversee a junior dev. I’d also add that you should implement "State Memory." If the Coder knows exactly what was rejected in the previous turn, it won't repeat the same error. Using a shared "Workspace" (like a temporary file system) where both agents can see the current project state is also vital for keeping them on the same page during a long-running task.