AI Agents Don't Fail at Reasoning — They Fail at State

AI agent failures are often attributed to poor reasoning or hallucinations, but the actual cause is typically the orchestration loop's reliance on chat transcripts as ground truth. These lossy summaries cause geometric error compounding. Architectural solutions include separating memory from belief and checkpointing verified state. Additionally, enterprise AI implementations face specific failure points. Addressing these structural issues is more effective than simply seeking smarter base models to prevent agents from looping or executing incorrect actions.