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Source note2 min read

Diagnosing with Insights: Structured Analysis of Agent Failures via Behavioral Abstractions

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OpenReview

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Plain-Language Brief

The paper presents AGENTSCOPE, a new approach to diagnose failures in Language Model (LLM) agents. This method uses structured abstractions of agent behavior based on trajectories, and neural invariants to specify properties of the agent's behavior. AGENTSCOPE outperforms current methods in fault localization and attribution accuracy on both public and self-created datasets.

What the Document Says

  • The paper introduces AGENTSCOPE, a neuro-symbolic approach for diagnosing failures in LLM agents.
  • AGENTSCOPE abstracts agent behavior based on trajectories into structured representations.
  • Neural invariants are used in AGENTSCOPE to specify properties of the agent's behavior.
  • AGENTSCOPE uses LLM-guided reasoning against neural invariants to pinpoint failure steps and types.
  • The effectiveness of AGENTSCOPE is demonstrated on publicly available datasets (Who&When) and a self-created dataset (AgentErrata).
  • AGENTSCOPE outperforms current methods in fault localization and attribution accuracy.

Key Concepts Mentioned

  • LLM agents
  • agent failure diagnosis
  • structured abstractions
  • neural invariants
  • LLM-guided reasoning
  • fault localization
  • attribution accuracy

Claims Made by the Document

  • AGENTSCOPE is effective for diagnosing failures in LLM agents.
  • AGENTSCOPE outperforms current methods in fault localization and attribution accuracy.

Evidence Used

  • The paper demonstrates the effectiveness of AGENTSCOPE on publicly available datasets (Who&When) and a self-created dataset (AgentErrata).
  • Results show that AGENTSCOPE significantly outperforms the current art in fault localization and attribution accuracy.

Methods / Approach

  • structured abstractions of agent behavior based on trajectories
  • neural invariants
  • LLM-guided reasoning

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The text does not provide information about limitations of the method as perceived by the agent.

Open Questions

  • What are the specific details of the AGENTSCOPE approach?
  • How is the self-created dataset (AgentErrata) different from the publicly available one (Who&When)?
  • What are the current methods AGENTSCOPE outperforms?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper does not provide details about the specifics of the AGENTSCOPE approach or the current methods it outperforms. More information about the self-created dataset (AgentErrata) compared to the publicly available one (Who&When) is also lacking.

Raw Abstract / Excerpt

With the proliferation of LLM agents, the ability to understand and diagnose failures in agents is essential to achieving superior effectiveness and trustworthiness. As agent failures often manifest via long and complex trajectories, manually finding the needles in the haystack is untenable. However, traditional diagnosis techniques for software bugs can hardly address LLM agent failures, while completely relying on LLMs as the judge yields unreliable diagnosis results. To overcome these challenges, this paper presents AGENTSCOPE, a new neuro-symbolic approach for agent failure mode diagnosis. The key principle of AGENTSCOPE is to abstract agent behavior, based on its trajectories, into structured representations. Furthermore, AGENTSCOPE introduces the concept of neural invariants to specify agent behavior properties. AGENTSCOPE leverages LLM-guided reasoning atop the structured representation against neural invariants to pinpoint both the failure step and its type in the trajectory. We show the effectiveness of AGENTSCOPE on publicly available agent failure datasets (Who&When) and a more comprehensive dataset created by us (AgentErrata), where AGENTSCOPE significantly outperforms the current art in fault localization and attribution accuracy. Our work shows that integrating structured abstractions with LLM-guided reasoning enables effective, reliable, and interpretable diagnosis for agent failures.