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7 Agent Failure Modes You Can Spot Early | by Quaxel
medium.com
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Press enter or click to view image in full size “Learn 7 early-detectable AI agent failure modes: looping, tool misuse, prompt injection, drift, hallucinated state, retries, and goal misalignment.” Agents don’t usually fail in spectacular ways.
They fail quietly. “They “kind of work” for a week, ship a few wins, and then one day they do something weird — send the wrong email, call the wrong tool, loop for 20 minutes, or confidently report a result that never happened.” And the worst part? Most of these failures were detectable before they became incidents.
And the worst part? Most of these failures were detectable before they became incidents. “Let’s be real: agent reliability is mostly observability + constraints.Not bigger models. Not more prompts. Not hoping.” Here are 7 agent failure modes you can detect early, with concrete signals, metrics, and guardrails that real teams use.
They “kind of work” for a week, ship a few wins, and then one day they do something weird — send the wrong email, call the wrong tool, loop for 20 minutes, or confidently report a result that never happened. “And the worst part? Most of these failures were detectable before they became incidents.” Let’s be real: agent reliability is mostly observability + constraints.Not bigger models. Not more prompts. Not hoping.
Agents don’t usually fail in spectacular ways. “They fail quietly.” They “kind of work” for a week, ship a few wins, and then one day they do something weird — send the wrong email, call the wrong tool, loop for 20 minutes, or confidently report a result that never happened.
Guardrails that actually help “Force a goal restatement every N steps: “What are we trying to achieve?”” Add a progress contract: required outputs (e.g., “must return JSON with fields X”)
Use circuit breakers: “if upstream is failing, stop trying for a cooldown window” Prefer partial completion with escalation: