Your agent thinks it's reasoning well.
It lost the thread five steps ago

Your agent applies the same reasoning to a 3-step task and a 30-step chain. It cannot shift gears. The cognitive strategy is frozen at deploy time.

Three-agent chain at 90% step accuracy: 73% end-to-end success. Five agents: 59%. Reasoning errors don't add. They compound, each hop inheriting and amplifying what broke upstream.

Reasoning failures don't throw exceptions. By the time wrong output surfaces, the agent has already made three more decisions on top of the bad one. There's no stack trace for cognition.

Models lose mid-prompt content. The guardrail you need is the one the model stopped reading by token 2,000. Your 5,000-token system prompt competes with itself.

The agent commits to its first interpretation and never self-corrects. On ARC-AGI-3, this is the #1 failure mode of every frontier model. The hypothesis feels right, so the agent stops questioning it.

The agent reaches the first plausible answer and presents it as final. No verification, no alternatives considered, no uncertainty flagged. In our benchmarks, baseline agents self-monitored on less than 25% of tasks.

Your agent reasons well about causality but misses the temporal dependency. Or nails the spatial layout but ignores the simulation consequences. Real tasks span multiple dimensions. Single-lens reasoning misses the intersection.