We built a public archive of AI failure patterns. The ones that keep coming back after changes.
Summary
An archive called Agent Fail Museum documents recurring AI failure patterns and provides regression test drafts for submitted failures, aiming to prevent repeat incidents.
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The author observes that AI agents exhibit human-like failure patterns, such as overconfidence and skipping steps under context pressure, suggesting that system reliability depends more on robust validation and controlled environments than just model intelligence.
AI agents fail in ways nobody writes about. Here's what I've actually seen.
The article highlights practical system-level failures in AI agent workflows, such as context bleed and hallucinated details, arguing that these are often infrastructure issues rather than model defects.
Something I keep seeing with AI projects that nobody talks about openly
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I analyzed how 50+ AI teams debug production agent failures and got surprised
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Built an Open-Source Tool That Finds Missing Validation, Retries, and Error Handling in AI Agent Systems
We released Trustabl Agent Analyzer, an open-source tool that scans AI agent repositories to find missing validation, retries, and error handling, generating a privacy-preserving local report.