Willful Disobedience: Automatically Detecting Failures in Agentic Traces
📰 ArXiv cs.AI
AgentPex automatically detects failures in agentic traces, improving validation of AI agents in software systems
Action Steps
- Collect agentic traces from AI agents executing multi-step workflows
- Analyze traces for procedural failures, such as incorrect workflow routing or unsafe tool usage
- Use AgentPex to automatically detect failures and validate AI agent behavior
- Integrate AgentPex into existing testing and validation pipelines to improve overall system reliability
Who Needs to Know This
AI engineers and researchers benefit from AgentPex as it helps identify critical procedural failures in agentic traces, while product managers and DevOps teams can use it to improve the reliability of AI-powered systems
Key Insight
💡 Procedural failures in agentic traces can be automatically detected using AgentPex, improving the validation of AI agents in software systems
Share This
🚨 Automatically detect failures in AI agent execution histories with AgentPex 💡
Key Takeaways
AgentPex automatically detects failures in agentic traces, improving validation of AI agents in software systems
Full Article
Title: Willful Disobedience: Automatically Detecting Failures in Agentic Traces
Abstract:
arXiv:2603.23806v1 Announce Type: cross Abstract: AI agents are increasingly embedded in real software systems, where they execute multi-step workflows through multi-turn dialogue, tool invocations, and intermediate decisions. These long execution histories, called agentic traces, make validation difficult. Outcome-only benchmarks can miss critical procedural failures, such as incorrect workflow routing, unsafe tool usage, or violations of prompt-specified rules. This paper presents AgentPex, an
Abstract:
arXiv:2603.23806v1 Announce Type: cross Abstract: AI agents are increasingly embedded in real software systems, where they execute multi-step workflows through multi-turn dialogue, tool invocations, and intermediate decisions. These long execution histories, called agentic traces, make validation difficult. Outcome-only benchmarks can miss critical procedural failures, such as incorrect workflow routing, unsafe tool usage, or violations of prompt-specified rules. This paper presents AgentPex, an
DeepCamp AI