AgentFixer: From Failure Detection to Fix Recommendations in LLM Agentic Systems

• URL: http://arxiv.org/abs/2603.29848v1
• Date: Wed, 18 Feb 2026 14:55:35 GMT
• Title: AgentFixer: From Failure Detection to Fix Recommendations in LLM Agentic Systems
• Abstract summary: The framework includes fifteen failure-detection tools and two root-cause analysis modules.<n>It integrates lightweight rule-based checks with LLM-as-a-judge assessments to support structured incident detection, classification, and repair.<n>We applied the framework to IBM CUGA, evaluating its performance on the AppWorld and WebArena benchmarks.
• Score: 7.429835301272413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a comprehensive validation framework for LLM-based agentic systems that provides systematic diagnosis and improvement of reliability failures. The framework includes fifteen failure-detection tools and two root-cause analysis modules that jointly uncover weaknesses across input handling, prompt design, and output generation. It integrates lightweight rule-based checks with LLM-as-a-judge assessments to support structured incident detection, classification, and repair. We applied the framework to IBM CUGA, evaluating its performance on the AppWorld and WebArena benchmarks. The analysis revealed recurrent planner misalignments, schema violations, brittle prompt dependencies, and more. Based on these insights, we refined both prompting and coding strategies, maintaining CUGA's benchmark results while enabling mid-sized models such as Llama 4 and Mistral Medium to achieve notable accuracy gains, substantially narrowing the gap with frontier models. Beyond quantitative validation, we conducted an exploratory study that fed the framework's diagnostic outputs and agent description into an LLM for self-reflection and prioritization. This interactive analysis produced actionable insights on recurring failure patterns and focus areas for improvement, demonstrating how validation itself can evolve into an agentic, dialogue-driven process. These results show a path toward scalable, quality assurance, and adaptive validation in production agentic systems, offering a foundation for more robust, interpretable, and self-improving agentic architectures.

Related papers

• From Atomic to Agentic: Towards Interpretable Evaluation of LLMs' Agentic Mathematical Capabilities [103.60384544335072]
  Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence.<n>Our framework aligns problem-solving agentic behaviors with a structured taxonomy of reusable mathematical atomic capabilities.<n> Experiments reveal that models with similar end-to-end accuracy can exhibit markedly different agentic capability profiles.
  arXiv  Detail & Related papers  (2026-08-27T10:52:06Z)
• Beyond LLM-Based Reasoning: Lightweight GNNs for Agent Failure Attribution [47.70578021328887]
  Large language model (LLM)-based multi-agent systems often exhibit complex failure modes.<n>We introduce AFANet, a lightweight graph-based framework that models interaction trajectories through step-level semantic signals and agent-level relationships.<n>Our results suggest that effective agent failure attribution does not require heavy reasoning and a lightweight, structured approach can achieve strong performance.
  arXiv  Detail & Related papers  (2026-08-19T06:16:09Z)
• VerifyMAS: Hypothesis Verification for Failure Attribution in LLM Multi-Agent Systems [79.51005192758262]
  Large language model-driven multi-agent systems excel at complex tasks.<n>However, unreliable agents remain a key bottleneck to system-level reliability.<n>We propose a hypothesis verification framework for agent failure attribution.
  arXiv  Detail & Related papers  (2026-05-17T14:09:35Z)
• A Self-Healing Framework for Reliable LLM-Based Autonomous Agents [0.0]
  This paper proposes a reliability-aware self-healing framework for LLM-based software agents.<n>The proposed framework was implemented in a multi-agent workflow environment and evaluated using real-world task scenarios.
  arXiv  Detail & Related papers  (2026-05-07T13:10:41Z)
• A Systematic Approach for Large Language Models Debugging [9.144050771609637]
  Large language models (LLMs) have become central to modern AI, powering applications from open-ended text generation to complex agent-based reasoning.<n>This paper introduces a systematic approach for LLM debug that treats models as observable systems.<n>By unifying evaluation, interpretability, and error-analysis practices, our approach enables practitioners to iteratively diagnose model weaknesses.
  arXiv  Detail & Related papers  (2026-04-24T21:30:33Z)
• Automated Self-Testing as a Quality Gate: Evidence-Driven Release Management for LLM Applications [51.56484100374058]
  We present an automated self-testing framework that introduces quality gates with evidence-based release decisions.<n>We evaluate the framework through a longitudinal case study of an internally deployed multi-agent conversational AI system.
  arXiv  Detail & Related papers  (2026-03-13T20:44:15Z)
• Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems [0.0]
  We present a case-aware LLM-as-a-Judge evaluation framework for enterprise multi-turn RAG systems.<n>The framework evaluates each turn using eight operationally grounded metrics that separate retrieval quality, grounding fidelity, answer utility, precision integrity, and case/workflow alignment.
  arXiv  Detail & Related papers  (2026-02-23T21:37:06Z)
• Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction [58.51530390018909]
  Large Language Model based multi-agent systems excel at collaborative problem solving but remain brittle to cascading errors.<n>We present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction.
  arXiv  Detail & Related papers  (2025-10-16T05:35:37Z)
• A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
  This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
  arXiv  Detail & Related papers  (2025-10-10T06:56:50Z)
• 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)
• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
  Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• Towards a Probabilistic Framework for Analyzing and Improving LLM-Enabled Software [0.0]
  Large language model (LLM)-enabled systems are a significant challenge in software engineering.<n>We propose a probabilistic framework for systematically analyzing and improving these systems.
  arXiv  Detail & Related papers  (2025-01-10T22:42:06Z)
• An Empirical Study on LLM-based Agents for Automated Bug Fixing [8.660251517380779]
  Large language models (LLMs) and LLM-based Agents have been applied to fix bugs automatically.<n>We examine six repair systems on the SWE-bench Verified benchmark for automated bug fixing.
  arXiv  Detail & Related papers  (2024-11-15T14:19:15Z)

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.