The automation failure nobody budgets for: the action landed, but the timeout said it failed
Summary
The article discusses a critical automation failure mode where actions succeed but responses time out, leading to duplicates, and advocates for using stable operation IDs and state checks to improve agent evaluation robustness.
Similar Articles
Your agent’s action timed out. Does your code retry it?
Technical article discussing the importance of retry logic when agent actions time out, highlighting a common pitfall in agent-based systems.
The agent failures that cost me the most all reported success
The author analyzed 155 AI agent jobs and discovered that most failures stemmed from infrastructure issues like timeouts and false success signals, not model errors, leading to practices such as asserting on effects and using multiple verification paths.
Anyone else finding that “agent said it succeeded” ≠ "it actually did the right thing"?
A practitioner asks about real-world experiences with AI agents reporting success while business outcomes are wrong, seeking operational feedback on manual checks and failure costs.
If you automated something and stopped checking it, did the errors stop, or did you just stop finding them?
The author reflects on conversations with people running AI automations, noting a pattern where verification is dropped after initial audits, which may hide silent failures. They ask for concrete stories about automations that were wrong without anyone noticing.
The agent failures that get you aren't crashes. They're clean runs that did the wrong thing.
The article discusses how AI agents often fail silently by completing tasks incorrectly without crashing, leading to undetected errors. It highlights common failure modes and explores potential detection strategies.