Agent Failure Classifier: Post-Hoc Root Cause Analysis for Failed LLM Agent Runs

1. 1Collect failure traces from LLM agent runs
2. 2Apply root cause analysis to identify failure patterns
3. 3Configure a classifier to predict failure causes
4. 4Test the classifier on new failure traces
5. 5Refine the classifier using feedback from users and agents

Machine learning engineers and researchers can benefit from this technique to debug and improve their LLM agents

💡 Post-hoc analysis of failure traces can help identify root causes of LLM agent failures

🤖 Improve LLM agent reliability with post-hoc root cause analysis and agent failure classification