I Built an AI Agent Team That Fixes Its Own Mistakes — Here’s the Full, Tested Code

A developer built SelfHeal, a multi-agent AI system using the Supervisor-Worker pattern that automatically recovers from worker agent failures by rerouting tasks to a fallback agent with a different strategy, logging the recovery process. The system is designed to handle edge cases in production environments where typical agent tutorials only show successful paths. The full tested code is available on GitHub.

Member-only story I Built an AI Agent Team That Fixes Its Own Mistakes — Here’s the Full, Tested Code Most agent tutorials show you the happy path. This one shows you what happens when an agent fails — and how to make the system heal itself instead of crashing. All codes given in thsi article are available on Github : Self-Healing-Agent- Every agentic AI demo looks the same: a clean prompt goes in, a perfect tool call comes out, everybody claps. Then you deploy it, a worker agent hits an edge case it wasn’t built for, throws an exception, and your “autonomous” system falls over in front of a customer. The pattern that separates toy agents from production agents isn’t a bigger model or a longer prompt. It’s what happens after something fails. In this article we build SelfHeal , a small but genuinely production-shaped multi-agent system that implements the Supervisor-Worker pattern with automatic error recovery : when a worker agent fails, the Supervisor doesn’t crash or return a raw stack trace — it catches the failure, reroutes the task to a fallback agent with a different strategy, and logs exactly how it healed itself. Every line of code in this article was written and executed in a sandboxed…