# AgentOps: Enabling Observability of LLM Agents arXiv: https://arxiv.org/abs/2411.05285 · HTML: https://mlsanity.com/paper/2411.05285/ ## Key facts - Cited by 46 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 6 of its 8 references that are in the map. - First posted on arXiv 8 Nov 2024. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 6 of 6) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [Visibility into AI Agents](https://mlsanity.com/paper/2401.13138/) (2024) - [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017) - [Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agents](https://mlsanity.com/paper/2405.10467/) (2024) - [Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents](https://mlsanity.com/paper/2311.13148/) (2023) - [Swiss Cheese Model for AI Safety: A Taxonomy and Reference Architecture for Multi-Layered Guardrails of Foundation Model Based Agents](https://mlsanity.com/paper/2408.02205/) (2024) ## Built on by (most cited first, 30 of 46) - [From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents](https://mlsanity.com/paper/2606.04990/) (2026) - [AgentSight: System-Level Observability for AI Agents Using eBPF](https://mlsanity.com/paper/2508.02736/) (2025) - [Auditable Agents](https://mlsanity.com/paper/2604.05485/) (2026) - [The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration](https://mlsanity.com/paper/2603.22862/) (2026) - [Characterizing Faults in Agentic AI: A Taxonomy of Types, Symptoms, and Root Causes](https://mlsanity.com/paper/2603.06847/) (2026) - [Evaluation-Driven Development and Operations of LLM Agents: A Process Model and Reference Architecture](https://mlsanity.com/paper/2411.13768/) (2024) - [TrajAudit: Automated Failure Diagnosis for Agentic Coding Systems](https://mlsanity.com/paper/2605.26563/) (2026) - [AgentTrace: A Structured Logging Framework for Agent System Observability](https://mlsanity.com/paper/2602.10133/) (2026) - [AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents](https://mlsanity.com/paper/2607.18754/) (2026) - [Beyond Task Completion: An Assessment Framework for Evaluating Agentic AI Systems](https://mlsanity.com/paper/2512.12791/) (2025) - [FlowSteer: Prompt-Only Workflow Steering Exposes Planning-Time Vulnerabilities in Multi-Agent LLM Systems](https://mlsanity.com/paper/2605.11514/) (2026) - [REFLECT: Intervention-Supported Error Attribution for Silent Failures in LLM Agent Traces](https://mlsanity.com/paper/2606.09071/) (2026) - [Oversight Structures for Agentic AI in Public-Sector Organizations](https://mlsanity.com/paper/2506.04836/) (2025) - [Agentic Services Computing](https://mlsanity.com/paper/2509.24380/) (2025) - [Towards Security-Auditable LLM Agents: A Unified Graph Representation](https://mlsanity.com/paper/2605.06812/) (2026) - [RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines](https://mlsanity.com/paper/2506.03401/) (2025) - [AI Agents Push Humans Out of the Loop](https://mlsanity.com/paper/2608.23642/) (2026) - [LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systems](https://mlsanity.com/paper/2508.12412/) (2025) - [ClawTrace: Cost-Aware Tracing for LLM Agent Skill Distillation](https://mlsanity.com/paper/2604.23853/) (2026) - [GRADE: Graph Representation of LLM Agent Dependency and Execution](https://mlsanity.com/paper/2606.22741/) (2026) - [The Y-Combinator for LLMs: Solving Long-Context Rot with λ-Calculus](https://mlsanity.com/paper/2603.20105/) (2026) - [Clawed and Dangerous: Can We Trust Open Agentic Systems?](https://mlsanity.com/paper/2603.26221/) (2026) - [Designing for Doubt: The Case for Informed Abstention in Autonomous Agents](https://mlsanity.com/paper/2606.02965/) (2026) - [Observability and Fault Injection for LLM-Based Multi-Agent Systems in Software Engineering](https://mlsanity.com/paper/2608.24271/) (2026) - [Repair or Resample? Rethinking Failure Debugging in LLM Multi-Agent Systems](https://mlsanity.com/paper/2608.25920/) (2026) - [How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study](https://mlsanity.com/paper/2607.10856/) (2026) - [Agentic Configuration Management (ACM): A Reference Configuration Model for Governed Agentic Systems](https://mlsanity.com/paper/2608.11166/) (2026) - [OpenDerisk: An Industrial Framework for AI-Driven SRE, with Design, Implementation, and Case Studies](https://mlsanity.com/paper/2510.13561/) (2025) - [HarmTransform: Transforming Explicit Harmful Queries into Stealthy via Multi-Agent Debate](https://mlsanity.com/paper/2512.23717/) (2025) - [Anonymous-by-Construction: An LLM-Driven Framework for Privacy-Preserving Text](https://mlsanity.com/paper/2603.17217/) (2026) ## Abstract (arXiv) Large language model (LLM) agents have demonstrated remarkable capabilities across various domains, gaining extensive attention from academia and industry. However, these agents raise significant concerns on AI safety due to their autonomous and non-deterministic behavior, as well as continuous evolving nature . From a DevOps perspective, enabling observability in agents is necessary to ensuring AI safety, as stakeholders can gain insights into the agents' inner workings, allowing them to proactively understand the agents, detect anomalies, and prevent potential failures. Therefore, in this paper, 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. The taxonomy is developed based on a systematic mapping study of existing AgentOps tools. Our taxonomy serves as a reference template for developers to design and implement AgentOps infrastructure that supports monitoring, logging, and analytics. thereby ensuring AI safety.