The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution

• URL: http://arxiv.org/abs/2601.15075v1
• Date: Wed, 21 Jan 2026 15:22:21 GMT
• Title: The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution
• Authors: Chen Qian, Peng Wang, Dongrui Liu, Junyao Yang, Dadi Guo, Ling Tang, Jilin Mei, Qihan Ren, Shuai Shao, Yong Liu, Jie Fu, Jing Shao, Xia Hu,
• Abstract summary: Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering.<n>We propose a novel framework for textbfgeneral agentic attribution, designed to identify the internal factors driving agent actions regardless of the task outcome.<n>We validate our framework across a diverse suite of agentic scenarios, including standard tool use and subtle reliability risks like memory-induced bias.
• Score: 63.61358761489141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering. As these systems become more autonomous and are deployed at scale, understanding why an agent takes a particular action becomes increasingly important for accountability and governance. However, existing research predominantly focuses on \textit{failure attribution} to localize explicit errors in unsuccessful trajectories, which is insufficient for explaining the reasoning behind agent behaviors. To bridge this gap, we propose a novel framework for \textbf{general agentic attribution}, designed to identify the internal factors driving agent actions regardless of the task outcome. Our framework operates hierarchically to manage the complexity of agent interactions. Specifically, at the \textit{component level}, we employ temporal likelihood dynamics to identify critical interaction steps; then at the \textit{sentence level}, we refine this localization using perturbation-based analysis to isolate the specific textual evidence. We validate our framework across a diverse suite of agentic scenarios, including standard tool use and subtle reliability risks like memory-induced bias. Experimental results demonstrate that the proposed framework reliably pinpoints pivotal historical events and sentences behind the agent behavior, offering a critical step toward safer and more accountable agentic systems.

Related papers

• Agentic Problem Frames: A Systematic Approach to Engineering Reliable Domain Agents [0.0]
  Large Language Models (LLMs) are evolving into autonomous agents, yet current "frameless" development--relying on ambiguous natural language--leads to critical risks such as scope creep and open-loop failures.<n>This study proposes Agentic Problem Frames (APF), a systematic engineering framework that shifts focus from internal model intelligence to the structured interaction between the agent and its environment.
  arXiv  Detail & Related papers  (2026-02-22T06:32:32Z)
• OMNI-LEAK: Orchestrator Multi-Agent Network Induced Data Leakage [59.3826294523924]
  We investigate the security vulnerabilities of a popular multi-agent pattern known as the orchestrator setup.<n>We report the susceptibility of frontier models to different categories of attacks, finding that both reasoning and non-reasoning models are vulnerable.
  arXiv  Detail & Related papers  (2026-02-13T21:32:32Z)
• AgentTrace: A Structured Logging Framework for Agent System Observability [0.0]
  AgentTrace is a dynamic observability and telemetry framework designed to fill this gap.<n>Unlike traditional logging systems, AgentTrace emphasizes continuous, introspectable trace capture.<n>Our research highlights how AgentTrace can enable more reliable agent deployment, fine-grained risk analysis, and informed trust calibration.
  arXiv  Detail & Related papers  (2026-02-07T04:04:59Z)
• Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs [65.6660735371212]
  We present textbftextscJustAsk, a framework that autonomously discovers effective extraction strategies through interaction alone.<n>It formulates extraction as an online exploration problem, using Upper Confidence Bound--based strategy selection and a hierarchical skill space spanning atomic probes and high-level orchestration.<n>Our results expose system prompts as a critical yet largely unprotected attack surface in modern agent systems.
  arXiv  Detail & Related papers  (2026-01-29T03:53:25Z)
• Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection [76.91230292971115]
  Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks.<n>XG-Guard is an explainable and fine-grained safeguarding framework for detecting malicious agents in MAS.
  arXiv  Detail & Related papers  (2025-12-21T13:46:36Z)
• Know your Trajectory -- Trustworthy Reinforcement Learning deployment through Importance-Based Trajectory Analysis [6.7842735984907465]
  We introduce a novel framework that ranks entire trajectories by defining and aggregating a new state-importance metric.<n>We demonstrate that our method successfully identifies optimal trajectories from a heterogeneous collection of agent experiences.<n>Our experiments in standard OpenAI Gym environments validate that our proposed importance metric is more effective at identifying optimal behaviors.
  arXiv  Detail & Related papers  (2025-12-07T16:52:08Z)
• AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering [51.07491603393163]
  tAgent is a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals.<n>By leveraging soft supervision and weighted aggregation of agent outputs, Agent learns principled collaboration schemes that capture the complementary strengths of diverse agents.
  arXiv  Detail & Related papers  (2025-10-06T23:20:49Z)
• Agentic Metacognition: Designing a "Self-Aware" Low-Code Agent for Failure Prediction and Human Handoff [0.0]
  Non-deterministic nature of autonomous agents presents reliability challenges.<n> secondary, "metacognitive" layer actively monitors primary LCNC agent.<n>Inspired by human introspection, this layer is designed to predict impending task failures.
  arXiv  Detail & Related papers  (2025-09-24T06:10:23Z)
• AgentCompass: Towards Reliable Evaluation of Agentic Workflows in Production [4.031479494871582]
  We present Agent, the first evaluation framework designed specifically for post-deployment monitoring and reasoning of agentic pipeline.<n>Agent achieves state-of-the-art results on key metrics, while uncovering critical issues missed in human annotations.
  arXiv  Detail & Related papers  (2025-09-18T05:59:04Z)
• Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories [17.975121612118752]
  Large Language Model (LLM)-based agents are increasingly employed to automate complex software engineering tasks.<n>We present a large-scale empirical study of the thought-action-result trajectories of three state-of-the-art LLM-based agents.<n>We identify key trajectory characteristics, such as counts and token consumption, recurring action sequences, and the semantic coherence of thoughts, actions, and their results.
  arXiv  Detail & Related papers  (2025-06-23T16:34:52Z)
• Agentic AI Process Observability: Discovering Behavioral Variability [2.273531916003657]
  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.
  arXiv  Detail & Related papers  (2025-05-26T15:26:07Z)
• Agent-as-a-Judge: Evaluate Agents with Agents [61.33974108405561]
  We introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We present DevAI, a new benchmark of 55 realistic automated AI development tasks.
  arXiv  Detail & Related papers  (2024-10-14T17:57:02Z)
• Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents [110.25679611755962]
  Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. We introduce Intention-in-Interaction (IN3), a novel benchmark designed to inspect users' implicit intentions through explicit queries. We empirically train Mistral-Interact, a powerful model that proactively assesses task vagueness, inquires user intentions, and refines them into actionable goals.
  arXiv  Detail & Related papers  (2024-02-14T14:36:30Z)

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.