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TRAIL: Trace Reasoning and Agentic Issue Localization | Failure Taxonomy | arXiv | 2025 | https://arxiv.org/abs/2505.08638 | https://github.com/patronus-ai/trail-benchmark | ||||||
Aegis: Taxonomy and Optimizations for Overcoming Agent-environment Failures in LLM Agents | Failure Taxonomy | arXiv | 2025 | https://arxiv.org/abs/2508.19504 | |||||||
Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks | Failure Taxonomy | ASE | 2025 | https://arxiv.org/abs/2508.13143 | |||||||
Where LLM Agents Fail and How They Can Learn From Failures | Failure Taxonomy | arXiv | 2025 | https://arxiv.org/abs/2509.25370 | https://github.com/ulab-uiuc/AgentDebug | ||||||
Why Do Multi-Agent LLM Systems Fail? | Failure Taxonomy | ICLR Workshop | 2025 | https://arxiv.org/abs/2503.13657 | https://github.com/multi-agent-systems-failure-taxonomy/MAST | ||||||
How Do LLMs Fail In Agentic Scenarios? A Qualitative Analysis of Success and Failure Scenarios of Various LLMs in Agentic Simulations | Failure Taxonomy | NeurIPS | 2025 | https://arxiv.org/abs/2512.07497 | |||||||
AgentRx: Diagnosing AI Agent Failures from Execution Trajectories | Failure Taxonomy | arXiv | 2026 | https://arxiv.org/abs/2602.02475 | |||||||
Demystifying the Lifecycle of Failures in Platform-Orchestrated Agentic Workflows | Failure Taxonomy | arXiv | 2025 | https://arxiv.org/abs/2509.23735v2 | |||||||
AgentEval: DAG-Structured Step-Level Evaluation for Agentic Workflows with Error Propagation Tracking | Failure Taxonomy | arXiv | 2026 | https://arxiv.org/abs/2604.23581 | |||||||
Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysis (FAMAS) | Failure Attribution Methods for LLM Agents | Pattern Analysis-Based | ESEC/FSE | 2026 | https://arxiv.org/abs/2509.13782 | ||||||
Traceability and Accountability in Role-Specialized Multi-Agent LLM Pipelines | Failure Attribution Methods for LLM Agents | Pattern Analysis-Based | arXiv | 2025 | https://arxiv.org/abs/2510.07614 | https://sites.google.com/view/mas-gain2025/home | |||||
CORRECT: Condensed eRror Recognition via Knowledge Transfer in Multi-Agent Systems | Failure Attribution Methods for LLM Agents | Pattern Analysis-Based | arXiv | 2025 | https://arxiv.org/abs/2509.24088 | ||||||
Scope Delineation Before Localization (SDBL) | Failure Attribution Methods for LLM Agents | Pattern Analysis-Based | AAAI | 2026 | https://arxiv.org/abs/2512.15374 | https://github.com/JarvisPei/SCOPE | |||||
AgentEval: DAG-Structured Step-Level Evaluation for Agentic Workflows with Error Propagation Tracking | Failure Attribution Methods for LLM Agents | Pattern Analysis-Based | arXiv | 2026 | https://arxiv.org/abs/2604.23581 | ||||||
ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics | Failure Attribution Methods for LLM Agents | Pattern Analysis-Based | arXiv | 2026 | https://arxiv.org/abs/2603.20260 | ||||||
Which Agent Causes Task Failures and When? | Failure Attribution Methods for LLM Agents | LLM Reasoning-Based | ICML | 2025 | https://arxiv.org/abs/2505.00212 | https://github.com/ag2ai/Agents_Failure_Attribution | |||||
Where Did It All Go Wrong? A Hierarchical Look into Multi-Agent Error Attribution (ECHO) | Failure Attribution Methods for LLM Agents | LLM Reasoning-Based | NeurIPS | 2025 | https://arxiv.org/abs/2510.04886 | ||||||
RAFFLES: Reasoning-based Attribution of Faults for LLM Systems | Failure Attribution Methods for LLM Agents | LLM Reasoning-Based | NeurIPS Workshop | 2025 | https://arxiv.org/abs/2509.06822 | ||||||
Automatic Failure Attribution and Critical Step Prediction based on Causal Inference (CDC-MAS) | Failure Attribution Methods for LLM Agents | LLM Reasoning-Based | arXiv | 2025 | https://arxiv.org/abs/2509.08682 | ||||||
Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems | Failure Attribution Methods for LLM Agents | LLM Reasoning-Based | NeurIPS | 2025 | https://arxiv.org/abs/2509.10401 | https://github.com/ResearAI/A2P | |||||
From Flat Logs to Causal Graphs: Hierarchical Failure Attribution for LLM-based Multi-Agent Systems | Failure Attribution Methods for LLM Agents | LLM Reasoning-Based | arXiv | 2026 | https://arxiv.org/abs/2602.23701 | ||||||
AgentRx: Diagnosing AI Agent Failures from Execution Trajectories | Failure Attribution Methods for LLM Agents | LLM Reasoning-Based | arXiv | 2026 | https://arxiv.org/abs/2602.02475 | ||||||
CodeTracer: Towards Traceable Agent States for Failure Attribution | Failure Attribution Methods for LLM Agents | LLM Reasoning-Based | arXiv | 2026 | https://arxiv.org/abs/2604.11641 | ||||||
ERRORPROBE: Towards Self-Improving Error Diagnosis in Multi-Agent Systems | Failure Attribution Methods for LLM Agents | LLM Reasoning-Based | arXiv | 2026 | https://arxiv.org/abs/2604.17658 | ||||||
AgenTracer: Who Is Inducing Failure in the LLM Agentic Systems? | Failure Attribution Methods for LLM Agents | Model Fine-Tuning-Based | ICLR | 2026 | https://arxiv.org/abs/2509.03312 | https://github.com/bingreeky/AgenTracer | |||||
GraphTracer: Graph-Guided Failure Tracing in LLM Agents | Failure Attribution Methods for LLM Agents | Model Fine-Tuning-Based | arXiv | 2025 | https://arxiv.org/abs/2510.10581 | ||||||
Aegis: Automated Error Generation and Attribution for Multi-Agent Systems | Failure Attribution Methods for LLM Agents | Model Fine-Tuning-Based | arXiv | 2025 | https://arxiv.org/abs/2509.14295 | ||||||
DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems | Failure Attribution Methods for LLM Agents | Dynamic Runtime-Based | ICLR | 2026 | https://arxiv.org/abs/2512.06749 | https://mbjinx.github.io/DoVer_Web/ | |||||
AgentDebug (Where LLM Agents Fail and How They can Learn From Failures) | Failure Attribution Methods for LLM Agents | Dynamic Runtime-Based | arXiv | 2025 | https://arxiv.org/abs/2509.25370 | https://github.com/ulab-uiuc/AgentDebug | |||||
TraceElephant: Seeing the Whole Elephant for Failure Attribution in LLM-based Multi-Agent Systems | Failure Attribution Methods for LLM Agents | Dynamic Runtime-Based | ACL | 2026 | https://arxiv.org/abs/2604.22708 | https://github.com/TraceElephant/TraceElephant | |||||
Demystifying the Lifecycle of Failures in Platform-Orchestrated Agentic Workflows | Failure Attribution Methods for LLM Agents | Dynamic Runtime-Based | arXiv | 2025 | https://arxiv.org/abs/2509.23735v2 | ||||||
Aegis: Taxonomy and Optimizations for Overcoming Agent-Environment Failures in LLM Agents | Enhancement, Optimization, and Repair | Structural and Workflow Optimization | arXiv | 2025 | https://arxiv.org/abs/2508.19504 | ||||||
Maestro: Joint Graph & Config Optimization for Reliable AI Agents | Enhancement, Optimization, and Repair | Structural and Workflow Optimization | arXiv | 2025 | https://arxiv.org/abs/2509.04642 | ||||||
Failure-Driven Workflow Refinement (CE-Graph) | Enhancement, Optimization, and Repair | Structural and Workflow Optimization | arXiv | 2025 | https://arxiv.org/abs/2510.10035 | ||||||
Instruction-Level Weight Shaping (ILWS) | Enhancement, Optimization, and Repair | Structural and Workflow Optimization | arXiv | 2025 | https://arxiv.org/abs/2509.00251 | ||||||
SCOPE: Prompt Evolution for Enhancing Agent Effectiveness | Enhancement, Optimization, and Repair | Agent Internal Optimization | arXiv | 2025 | https://arxiv.org/abs/2512.15374 | https://github.com/JarvisPei/SCOPE | |||||
AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering | Enhancement, Optimization, and Repair | Agent Internal Optimization | arXiv | 2026 | https://arxiv.org/abs/2601.04620 | ||||||
ReCreate: Reasoning and Creating Domain Agents Driven by Experience | Enhancement, Optimization, and Repair | Agent Internal Optimization | arXiv | 2026 | https://arxiv.org/abs/2601.11100 | https://github.com/zz-haooo/ReCreate | |||||
SEAlign: Alignment Training for Software Engineering Agent | Enhancement, Optimization, and Repair | Agent Internal Optimization | ICSE | 2026 | |||||||
Trajectory-Informed Memory Generation for Self-Improving Agent Systems | Enhancement, Optimization, and Repair | Agent Internal Optimization | arXiv | 2026 | https://arxiv.org/abs/2603.10600 | ||||||
Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills | Enhancement, Optimization, and Repair | Agent Internal Optimization | arXiv | 2026 | https://arxiv.org/abs/2603.25158 | ||||||
Improving the Efficiency of LLM Agent Systems through Trajectory Reduction (AgentDiet) | Enhancement, Optimization, and Repair | Runtime and Supervisory Optimization | arXiv | 2025 | https://arxiv.org/abs/2509.23586 | ||||||
Stop Wasting Your Tokens: Towards Efficient Runtime Multi-Agent Systems (SUPERVISOR AGENT) | Enhancement, Optimization, and Repair | Runtime and Supervisory Optimization | arXiv | 2025 | https://arxiv.org/abs/2510.26585 | ||||||
Wink: Recovering from Misbehaviors in Coding Agents | Enhancement, Optimization, and Repair | Runtime and Supervisory Optimization | arXiv | 2026 | https://arxiv.org/abs/2602.17037 | ||||||
Process-Centric Analysis of Agentic Software Systems | Enhancement, Optimization, and Repair | Runtime and Supervisory Optimization | OOPSLA | 2026 | |||||||
AgentSight: System-Level Observability for AI Agents using eBPF | Trajectory Monitoring, Debugging, and Analysis Tools | System-Level Monitoring and Passive Diagnosis | Workshop | 2025 | https://dl.acm.org/doi/10.1145/3766882.3767169 | https://github.com/eunomia-bpf/agentsight | https://dl.acm.org/doi/10.1145/3766882.3767169 | ||||
Taming Uncertainty via Automation: Observing, Analyzing, and Optimizing Agentic AI Systems | Trajectory Monitoring, Debugging, and Analysis Tools | System-Level Monitoring and Passive Diagnosis | arXiv | 2025 | https://arxiv.org/abs/2507.11277 | ||||||
AgentDiagnose: An Open Toolkit for Diagnosing LLM Agent Trajectories | Trajectory Monitoring, Debugging, and Analysis Tools | System-Level Monitoring and Passive Diagnosis | EMNLP | 2025 | https://aclanthology.org/2025.emnlp-demos.15/ | https://github.com/oootttyyy/AgentDiagnose | |||||
Agent Trajectory Explorer: Visualizing and Providing Feedback on Agent Trajectories | Trajectory Monitoring, Debugging, and Analysis Tools | System-Level Monitoring and Passive Diagnosis | AAAI | 2025 | https://doi.org/10.1609/aaai.v39i28.35350 | https://doi.org/10.1609/aaai.v39i28.35350 | |||||
Interactive Debugging and Steering of Multi-Agent AI Systems (AGDebugger) | Trajectory Monitoring, Debugging, and Analysis Tools | Interactive Analysis and Active Debugging | CHI | 2025 | https://doi.org/10.1145/3706598.3713581 | https://github.com/microsoft/agdebugger | https://doi.org/10.1145/3706598.3713581 | ||||
XAgen: An Explainability Tool for Identifying and Correcting Failures in Multi-Agent Workflows | Trajectory Monitoring, Debugging, and Analysis Tools | Interactive Analysis and Active Debugging | CHI | 2025 | https://arxiv.org/abs/2512.17896 | ||||||
DiLLS: Interactive Diagnosis of LLM-based Multi-agent Systems via Layered Summary of Agent Behaviors | Trajectory Monitoring, Debugging, and Analysis Tools | Interactive Analysis and Active Debugging | CHI | 2026 | https://arxiv.org/abs/2602.05446 | ||||||
AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents | Datasets and Benchmarks for Failure Attribution and Repair | Process and Span-Level Error Localization | arXiv | 2026 | 1,000 tool-augmented agent trajectories with 8,509 human-labeled step annotations for step-level process quality diagnosis. | https://arxiv.org/abs/2603.14465 | https://github.com/RUCBM/AgentProcessBench | ||||
CodeTracer / CodeTraceBench: Towards Traceable Agent States | Datasets and Benchmarks for Failure Attribution and Repair | Process and Span-Level Error Localization | arXiv; Hugging Face Dataset | 2026 | 4,316 coding-agent trajectories with human-verified stage- and step-level annotations for failure localization. | https://arxiv.org/abs/2604.11641 | https://huggingface.co/datasets/NJU-LINK/CodeTraceBench | ||||
TELBench / DRIFT: Where Do Deep-Research Agents Go Wrong? Span-Level Error Localization in Agent Trajectories | Datasets and Benchmarks for Failure Attribution and Repair | Process and Span-Level Error Localization | arXiv; Hugging Face Dataset | 2026 | 1,000 expert-verified deep-research trajectories with semantic spans and harmful error-span annotations. | https://arxiv.org/abs/2606.02060 | https://github.com/NJU-LINK/DRIFT | https://huggingface.co/datasets/NJU-LINK/TELBench | |||
ContextBench: A Benchmark for Context Retrieval in Coding Agents | Datasets and Benchmarks for Failure Attribution and Repair | Context Retrieval Benchmarks | arXiv | 2026 | 1,136 issue-resolution tasks across 66 repositories and 8 programming languages, with 4,548 files, 23,116 blocks, and 522,115 lines of human-verified gold contexts. | https://arxiv.org/abs/2602.05892 | https://github.com/EuniAI/ContextBench | ||||
Who&When | Datasets and Benchmarks for Failure Attribution and Repair | Real-World Failure Collection | ICML | 2025 | 127 trajectories | https://arxiv.org/abs/2505.00212 | https://github.com/ag2ai/Agents_Failure_Attribution | ||||
TRAIL | Datasets and Benchmarks for Failure Attribution and Repair | Real-World Failure Collection | arXiv | 2025 | 148 trajectories | https://arxiv.org/abs/2505.08638 | https://github.com/patronus-ai/trail-benchmark | ||||
AgentErrorBench | Datasets and Benchmarks for Failure Attribution and Repair | Real-World Failure Collection | arXiv | 2025 | 200 trajectories | https://arxiv.org/abs/2509.25370 | https://github.com/ulab-uiuc/AgentDebug | ||||
TraceElephant | Datasets and Benchmarks for Failure Attribution and Repair | Real-World Failure Collection | ACL | 2026 | 220 trajectories, full observability + reproducible environment | https://arxiv.org/abs/2604.22708 | https://github.com/TraceElephant/TraceElephant | ||||
Demystifying the Lifecycle of Failures in Platform-Orchestrated Agentic Workflows | Datasets and Benchmarks for Failure Attribution and Repair | Real-World Failure Collection | arXiv | 2025 | 307 trajectories, lifecycle-level annotation + repair strategy | https://arxiv.org/abs/2509.23735v2 | |||||
AgentRx | Datasets and Benchmarks for Failure Attribution and Repair | Real-World Failure Collection | arXiv | 2026 | first unrecoverable failure step annotation | https://arxiv.org/abs/2602.02475 | |||||
CodeTraceBench | Datasets and Benchmarks for Failure Attribution and Repair | Real-World Failure Collection | arXiv | 2026 | software-engineering agent traces for debugging and patching | https://arxiv.org/abs/2604.11641 | |||||
MP-Bench | Datasets and Benchmarks for Failure Attribution and Repair | Real-World Failure Collection | arXiv | 2026 | multi-perspective attribution with failure reason and ideal action annotations | https://arxiv.org/abs/2603.25001 | |||||
Aegis | Datasets and Benchmarks for Failure Attribution and Repair | Synthetic Data via Error Injection | arXiv | 2025 | 9,533 trajectories | https://arxiv.org/abs/2509.14295 | |||||
CORRECT-Error | Datasets and Benchmarks for Failure Attribution and Repair | Synthetic Data via Error Injection | arXiv | 2025 | 2,000+ trajectories | https://arxiv.org/abs/2509.24088 | |||||
Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories | Others and Empirical Studies | ASE | 2025 | https://arxiv.org/abs/2506.18824 | |||||||
MAESTRO: Multi-Agent Evaluation Suite for Testing, Reliability, and Observability | Others and Empirical Studies | arXiv | 2026 | https://arxiv.org/abs/2601.00481 | https://github.com/sands-lab/maestro | ||||||
Trajectory Guard — A Lightweight, Sequence-Aware Model for Real-Time Anomaly Detection in Agentic AI | Others and Empirical Studies | AAAI Workshop | 2026 | https://arxiv.org/abs/2601.00516 | |||||||
From Features to Actions: Explainability in Traditional and Agentic AI Systems | Others and Empirical Studies | arXiv | 2026 | https://arxiv.org/abs/2602.06841 | https://vectorinstitute.github.io/unified-xai-evaluation-framework/ | ||||||
Signals: Trajectory Sampling and Triage for Agentic Interactions | Others and Empirical Studies | arXiv | 2026 | https://arxiv.org/abs/2604.00356 | |||||||
Beyond Final Code: A Process-Oriented Error Analysis of Software Development Agents in Real-World GitHub Scenarios | Others and Empirical Studies | arXiv | 2025 | https://arxiv.org/abs/2503.12374 | |||||||
Beyond Resolution Rates: Behavioral Drivers of Coding Agent Success and Failure | Others and Empirical Studies | arXiv | 2026 | https://arxiv.org/abs/2604.02547 |
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