Agents_Failure_Attribution
by ag2ai
Automated failure attribution benchmark for multi-agent systems
Overview
Automates failure attribution in multi-agent systems by benchmarking how agentic workflows fail and why. Runs controlled multi-agent scenarios, collects interaction traces, and applies automated attribution algorithms to label root causes and failure modes. Includes a reproducible dataset, scoring metrics for attribution quality, and example pipelines built on popular LLM backends. For alignment with established patterns, consider using the A2A Protocol Pattern and the LLM-as-Judge Pattern as reference implementations A2A Protocol Pattern LLM-as-Judge Pattern.
Key Benefits
Ideal For
Researchers and engineering teams evaluating agent-to-agent reliability who need reproducible failure datasets and attribution metrics. This work complements established patterns like the Mutual Verification Pattern to ensure robust trust between agents.
Real-World Examples
- Diagnosing whether failures stem from delegation, hallucination, or coordination breakdowns
- Evaluating and comparing attribution methods across LLM backends and multi-agent designs
- Generating reproducible failure datasets to train reputation or mitigation systems