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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…