Article: When AI agents go spectacularly wrong—and what to do about it by Tobie Morgan Hitchcock

▤ Summary

This article argues that AI agents fail because they lack structured context, authority, and guardrails rather than raw language ability. It uses examples from Anthropic, Andon Labs, and Replit to show how agents can take costly or unsafe actions when they operate without clear business rules. The piece calls for grounded data, boundaries, observability, escalation paths, and a better infrastructure layer built around relationships and provenance. It also frames reliable agentic AI as a systems problem for enterprises that want to deploy automation safely. The core message is that businesses need operating environments that help agents act correctly, not just more prompting.

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