AI Agent Debugging Checklist: From Failed Run to Root Cause
📰 Dev.to AI
When an AI agent fails in production, the first instinct is usually to tweak the prompt and rerun the workflow. That can make the incident harder to understand. The rerun may change the model output, retrieved context, tool state, timing, permissions, or external API response. If the agent already sent an email, issued a refund, changed a ticket, or called an MCP tool, a naive rerun can also repeat a side effect. A better workflow starts by preserving evidence from the fa
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When an AI agent fails in production, the first instinct is usually to tweak the prompt and rerun the workflow. That can make the incident harder to understand. The rerun may change the model output, retrieved context, tool state, timing, permissions, or external API response. If the agent already sent an email, issued a refund, changed a ticket, or called an MCP tool, a naive rerun can also repeat a side effect. A better workflow starts by preserving evidence from the fa
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