Turn Production Agent Failures Into Eval Cases
percoAi · reddit · 2026-07-20
The author points out that most teams currently have limited observability over production-grade AI agents: they treat runtime failures merely as debugging issues (checking logs, tweaking prompts) rather than converting them into valuable product data.
Core Philosophy
A single failure is more than just an engineering footprint; it is precious learning data. It exposes deep-rooted issues like ambiguous task boundaries, misalignment between user expectations and strategies, overly loose tool contracts, lack of human-in-the-loop checkpoints, or missing recovery paths.
Actionable Advice
Teams should establish a robust feedback loop, translating every failure into structured data that includes:
- Root cause labels and missing evidence
- Flaws in strategy and tool contracts
- Missing Eval test cases to be added
- Optimization suggestions for product workflows
By doing so, sporadic system failures become the driving force for continuously iterating Agent logic, product workflows, and evaluation frameworks.
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