Agentic AI Process Observability: Discovering Behavioral Variability

• URL: http://arxiv.org/abs/2505.20127v2
• Date: Thu, 03 Jul 2025 10:05:46 GMT
• Title: Agentic AI Process Observability: Discovering Behavioral Variability
• Authors: Fabiana Fournier, Lior Limonad, Yuval David,
• Abstract summary: AI agents that leverage Large Language Models (LLMs) are increasingly becoming core building blocks of modern software systems.<n> frameworks enable the definition of agent setups using natural language prompting.<n>Within such setups, agent behavior is non-deterministic for any given input.
• Score: 2.273531916003657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI agents that leverage Large Language Models (LLMs) are increasingly becoming core building blocks of modern software systems. A wide range of frameworks is now available to support the specification of such applications. These frameworks enable the definition of agent setups using natural language prompting, which specifies the roles, goals, and tools assigned to the various agents involved. Within such setups, agent behavior is non-deterministic for any given input, highlighting the critical need for robust debugging and observability tools. In this work, we explore the use of process and causal discovery applied to agent execution trajectories as a means of enhancing developer observability. This approach aids in monitoring and understanding the emergent variability in agent behavior. Additionally, we complement this with LLM-based static analysis techniques to distinguish between intended and unintended behavioral variability. We argue that such instrumentation is essential for giving developers greater control over evolving specifications and for identifying aspects of functionality that may require more precise and explicit definitions.

Related papers

• AgentSight: System-Level Observability for AI Agents Using eBPF [10.37440633887049]
  Existing tools observe either an agent's high-level intent (via LLM prompts) or its low-level actions (e.g., system calls) but cannot correlate these two views.<n>We introduce AgentSight, an AgentOps observability framework that bridges this semantic gap using a hybrid approach.<n>AgentSight intercepts TLS-encrypted LLM traffic to extract semantic intent, monitors kernel events to observe system-wide effects, and causally correlates these two streams across process boundaries.
  arXiv  Detail & Related papers  (2025-08-02T01:43:39Z)
• A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence [87.08051686357206]
  Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
  arXiv  Detail & Related papers  (2025-07-28T17:59:05Z)
• Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories [18.129031749321058]
  Large Language Model (LLM)-based agents are increasingly employed to automate complex software engineering tasks.<n>Despite their widespread adoption, the internal decision-making processes of these agents remain largely unexplored.<n>We present a large-scale empirical study of the thought-action-result trajectories of three state-of-the-art LLM-based agents.
  arXiv  Detail & Related papers  (2025-06-23T16:34:52Z)
• Interactive Agents to Overcome Ambiguity in Software Engineering [61.40183840499932]
  AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions.<n>Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes.<n>We study the ability of LLM agents to handle ambiguous instructions in interactive code generation settings by evaluating proprietary and open-weight models on their performance.
  arXiv  Detail & Related papers  (2025-02-18T17:12:26Z)
• Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger [49.81945268343162]
  We propose MeCo, an adaptive decision-making strategy for external tool use.<n>MeCo captures high-level cognitive signals in the representation space, guiding when to invoke tools.<n>Our experiments show that MeCo accurately detects LLMs' internal cognitive signals and significantly improves tool-use decision-making.
  arXiv  Detail & Related papers  (2025-02-18T15:45:01Z)
• AgentOps: Enabling Observability of LLM Agents [12.49728300301026]
  Large language model (LLM) agents raise significant concerns on AI safety due to their autonomous and non-deterministic behavior.<n>We present a comprehensive taxonomy of AgentOps, identifying the artifacts and associated data that should be traced throughout the entire lifecycle of agents to achieve effective observability.<n>Our taxonomy serves as a reference template for developers to design and implement AgentOps infrastructure that supports monitoring, logging, and analytics.
  arXiv  Detail & Related papers  (2024-11-08T02:31:03Z)
• Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents [7.392058124132526]
  Foundations models (FMs) play an increasingly prominent role in complex software systems, such as agentic software.<n>Fast-thinking Large Language Models (LLMs) are still preferred due to latency constraints.<n>We introduce Watson, a framework that provides reasoning observability into implicit reasoning processes.
  arXiv  Detail & Related papers  (2024-11-05T19:13:22Z)
• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [117.94654815220404]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine.<n>G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)
• REVEAL-IT: REinforcement learning with Visibility of Evolving Agent poLicy for InTerpretability [23.81322529587759]
  REVEAL-IT is a novel framework for explaining the learning process of an agent in complex environments. We visualize the policy structure and the agent's learning process for various training tasks. A GNN-based explainer learns to highlight the most important section of the policy, providing a more clear and robust explanation of the agent's learning process.
  arXiv  Detail & Related papers  (2024-06-20T11:29:26Z)
• AgentScope: A Flexible yet Robust Multi-Agent Platform [66.64116117163755]
  AgentScope is a developer-centric multi-agent platform with message exchange as its core communication mechanism. The abundant syntactic tools, built-in agents and service functions, user-friendly interfaces for application demonstration and utility monitor, zero-code programming workstation, and automatic prompt tuning mechanism significantly lower the barriers to both development and deployment.
  arXiv  Detail & Related papers  (2024-02-21T04:11:28Z)
• Formally Specifying the High-Level Behavior of LLM-Based Agents [24.645319505305316]
  LLMs have emerged as promising tools for solving challenging problems without the need for task-specific finetuned models. Currently, the design and implementation of such agents is ad hoc, as the wide variety of tasks that LLM-based agents may be applied to naturally means there can be no one-size-fits-all approach to agent design. We propose a minimalistic generation framework that simplifies the process of building agents.
  arXiv  Detail & Related papers  (2023-10-12T17:24:15Z)
• Realistic simulation of users for IT systems in cyber ranges [63.20765930558542]
  We instrument each machine by means of an external agent to generate user activity. This agent combines both deterministic and deep learning based methods to adapt to different environment. We also propose conditional text generation models to facilitate the creation of conversations and documents.
  arXiv  Detail & Related papers  (2021-11-23T10:53:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.