Half of AI Agents Fail in Production. Most Teams Don't Even Know It.
Multiple practitioners are converging on the same diagnosis: AI agents look great in demos but collapse silently in deployment, with no observability, no state management, and no feedback loops to catch the failures before they compound.
A stark pattern is emerging across the AI agent ecosystem: the agents that dazzle in demos are quietly falling apart in production, and the teams deploying them often have no way of knowing. Patrick Kelly's analysis, shared by @analyseasia, puts a number on the problem — 50% of AI agents fail in production — and names the mechanism: "silent failure," where outputs degrade, reliability erodes, and downstream impact goes unmeasured because nobody built the instrumentation to catch it.
The diagnosis is reinforced by @polsia, who reports having observed more than 1,100 AI agents fail in the same ways. The failure modes aren't exotic. They're architectural: no observability into what the agent is actually doing step-by-step, no state management to track what the agent knows and has decided across turns, and no feedback loops to route errors back into improvement. These are the basics of production software engineering, and most agent deployments are shipping without them.
Get our free daily newsletter
Get this article free — plus the lead story every day — delivered to your inbox.
Want every article and the full archive? Upgrade anytime.
No spam. Unsubscribe anytime.