StepFinder turns execution logs into temporal semantic sequences via LLMs then uses temporal modeling plus attention to attribute failures to specific steps more accurately and 79% faster than direct LLM methods on the Who&When benchmark.
Automatic failure attribution and critical step prediction method for multi-agent systems based on causal inference,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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This survey categorizes anomalies in agent systems into intra-agent and inter-agent types and introduces the AgentOps framework with four operational stages.
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Agent System Operations: Categorization, Challenges, and Future Directions
This survey categorizes anomalies in agent systems into intra-agent and inter-agent types and introduces the AgentOps framework with four operational stages.