| title,category,subcategory,venue,year,paper_url,code_url,dataset_url,project_url,doi_url,other_urls,note | |
| 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,,,,,, | |
| 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,,,,, | |
| 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,,,,,, | |
| 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,,,,, | |
| 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,,,,,, | |
| 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,,,,,, | |
| Who&When,Datasets and Benchmarks for Failure Attribution and Repair,Real-World Failure Collection,ICML,2025,https://arxiv.org/abs/2505.00212,https://github.com/ag2ai/Agents_Failure_Attribution,,,,,127 trajectories | |
| TRAIL,Datasets and Benchmarks for Failure Attribution and Repair,Real-World Failure Collection,arXiv,2025,https://arxiv.org/abs/2505.08638,https://github.com/patronus-ai/trail-benchmark,,,,,148 trajectories | |
| AgentErrorBench,Datasets and Benchmarks for Failure Attribution and Repair,Real-World Failure Collection,arXiv,2025,https://arxiv.org/abs/2509.25370,https://github.com/ulab-uiuc/AgentDebug,,,,,200 trajectories | |
| TraceElephant,Datasets and Benchmarks for Failure Attribution and Repair,Real-World Failure Collection,ACL,2026,https://arxiv.org/abs/2604.22708,https://github.com/TraceElephant/TraceElephant,,,,,"220 trajectories, full observability + reproducible environment" | |
| Demystifying the Lifecycle of Failures in Platform-Orchestrated Agentic Workflows,Datasets and Benchmarks for Failure Attribution and Repair,Real-World Failure Collection,arXiv,2025,https://arxiv.org/abs/2509.23735v2,,,,,,"307 trajectories, lifecycle-level annotation + repair strategy" | |
| AgentRx,Datasets and Benchmarks for Failure Attribution and Repair,Real-World Failure Collection,arXiv,2026,https://arxiv.org/abs/2602.02475,,,,,,first unrecoverable failure step annotation | |
| Aegis,Datasets and Benchmarks for Failure Attribution and Repair,Synthetic Data via Error Injection,arXiv,2025,https://arxiv.org/abs/2509.14295,,,,,,"9,533 trajectories" | |
| CORRECT-Error,Datasets and Benchmarks for Failure Attribution and Repair,Synthetic Data via Error Injection,arXiv,2025,https://arxiv.org/abs/2509.24088,,,,,,"2,000+ trajectories" | |
| 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/,,, | |