What problem does it solve?
It prevents AI agents from failing when models hallucinate, tool outputs break schemas, or upstream APIs hit transient errors, so production workflows keep running.
Core Features & Use Cases
- Error classification before recovery: Detects transient, structural, model, and unrecoverable failures to choose the right recovery strategy.
- Strategy mutation and fallback models: Retries with backoff for transient issues, mutates the prompt/approach for structural errors, and switches models for model errors.
- Output validation with auto-repair and graceful degradation: Validates responses against expected structure, repairs missing fields when possible, and returns partial results with clear error reports when full recovery is impossible.
Example: An autonomous Google Ads analysis workflow calls multiple APIs and requires strict JSON outputs; when a schema changes or an LLM returns invalid structure, the agent classifies the failure, mutates the strategy or selects a fallback model, and salvages a usable result instead of aborting.
Quick Start
Build an autonomous agent that validates every tool and model output against a strict schema, then automatically retries, mutates strategy, or falls back models based on classified failure types.
