Why Your Agent Loops: Debugging AI Agents

Agent failures look chaotic — forty turns of confident wrongness — but they're systematic underneath. A handful of failure modes account for nearly everything, each with a recognizable signature in the trace and a fix at the class level. Debugging agents is mostly learning to read those signatures. (This page covers agents you're building; for Claude Code product issues, see its troubleshooting guide.)

The loop

Signature: the same tool call (or trivial variations) repeating; token spend climbing; no state change. Cause: broken feedback — the agent either can't tell the action failed (uninformative errors) or can't tell it succeeded (no confirmation in the observation), so its policy never updates. Fix, in order: make tools return informative, differentiated errors and changed-state confirmation (the tool-calling discipline); add loop detection (identical call twice → inject "this approach is failing, change strategy"); cap turns as the backstop, treating the cap as a failed run to diagnose, not retry blindly.

Premature victory

Signature: "I've successfully…" over work that doesn't compile, a test never run, a file never written. Cause: no verifiable completion condition — optimism fills the vacuum. Fix: define done as something executable and require the agent to run it before concluding ("done = npm test exits 0"). For tasks without a natural check, bolt one on: a fresh-context critic judging output against the original ask catches the self-grading bias.

Tool misuse

Signature: right intention, wrong tool — or tools ignored in favor of training-data guesses. Cause: routing happens by matching intent against tool names/descriptions; overlap and vagueness break it. Fix: disjoint, use-when/not-when descriptions, verb-object names, fewer overlapping tools. The test: read only your tool list — if you couldn't route correctly from it, the model can't.

Context poisoning

Signature: an early wrong fact ("the DB is MySQL") restated turn after turn, surviving corrections, steering everything. Cause: transformer attention treats repeated context as established truth; corrections compete with N restatements. Fix: don't argue — restart. Checkpoint before long runs, and on poisoning, clear and relaunch with the correction stated first. Prevention: pass constraints explicitly at task start (subagents inherit nothing), and persist ground truth to files the agent re-reads rather than trusting conversational memory.

Scope creep and the silent extras

Signature: the task done — plus a refactor nobody asked for, three "improved" files, a new dependency. Cause: helpfulness bias plus visible-but-irrelevant context. Fix: explicit boundaries in the task ("change only X; do not touch Y"), permission rules that make out-of-scope edits impossible, and diff review that treats unrequested changes as defects regardless of quality.

Make it stay fixed

Single-run fixes decay; the durable loop is: trace → classify → fix the class → encode the lesson — the tool description sharpened, the constraint added to the standing prompt, the verification step made mandatory, the eval case added so the regression is caught next time. That discipline — failure modes as a checklist applied before production — is exactly what the agent-reliability-reviewer automates.