arXiv:2605.21347v1 Announce Type: cross Abstract: Diagnosing failures in LLM agents remains largely manual. Practitioners inspect a small subset of execution traces, form ad-hoc hypotheses, and iterate. This process misses patterns that only emerge across trace populations and does not scale to production corpora where individual traces span tens of thousands of tokens. We formalize the problem of corpus-level trace diagnostics. Given a corpus of execution traces, the goal is to produce grounded natural-language insights that characterize systematic behavioral patterns across trace groups, eac
The rapid deployment and increasing complexity of LLM agents in production environments have made their failure diagnosis a critical bottleneck.
This development addresses a key challenge in scaling AI agent deployment, moving from ad-hoc debugging to systematic and scalable diagnostic methods.
The ability to perform corpus-level trace diagnostics will enable more robust and reliable AI agent systems, accelerating their integration into complex workflows.
- · AI agent developers
- · Enterprises deploying LLMs
- · AI software tool vendors
- · Manual debugging processes
- · Companies with low AI agent reliability
Improved reliability and performance of AI agents in production.
Faster iteration cycles for AI agent development and commercialization.
Accelerated adoption of AI agents across various industries due to increased trustworthiness and manageability.
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