AgentOps: Enabling Observability of LLM Agents

• URL: http://arxiv.org/abs/2411.05285v2
• Date: Sat, 30 Nov 2024 02:55:48 GMT
• Title: AgentOps: Enabling Observability of LLM Agents
• Authors: Liming Dong, Qinghua Lu, Liming Zhu,
• Abstract summary: Large language model (LLM) agents raise significant concerns on AI safety due to their autonomous and non-deterministic behavior. 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. Our taxonomy serves as a reference template for developers to design and implement AgentOps infrastructure that supports monitoring, logging, and analytics.
• Score: 12.49728300301026
• License:
• Abstract: 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.

Related papers

• AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds [12.464941027105306]
  AI for IT Operations (AIOps) aims to automate complex operational tasks, such as fault localization and root cause analysis, to reduce human workload and minimize customer impact. Recent advances in Large Language Models (LLMs) and AI agents are revolutionizing AIOps by enabling end-to-end and multitask automation. We present AIOPSLAB, a framework that deploys microservice cloud environments, injects faults, generates workloads, and exports telemetry data but also orchestrates these components and provides interfaces for interacting with and evaluating agents.
  arXiv  Detail & Related papers  (2025-01-12T04:17:39Z)
• Watson: A Cognitive Observability Framework for the Reasoning of Foundation Model-Powered Agents [7.392058124132526]
  Foundations models (FMs) play an increasingly prominent role in complex software systems, such as FM-powered agentic software (i.e., Agentware) Unlike traditional software, agents operate autonomously, using opaque data and implicit reasoning, making it difficult to observe and understand their behavior during runtime. We propose cognitive observability as a new type of required observability that has emerged for such innovative systems.
  arXiv  Detail & Related papers  (2024-11-05T19:13:22Z)
• Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance [95.03771007780976]
  We tackle the challenge of developing proactive agents capable of anticipating and initiating tasks without explicit human instructions. First, we collect real-world human activities to generate proactive task predictions. These predictions are labeled by human annotators as either accepted or rejected. The labeled data is used to train a reward model that simulates human judgment.
  arXiv  Detail & Related papers  (2024-10-16T08:24:09Z)
• 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)
• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [117.94654815220404]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine. G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)
• Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends [64.57762280003618]
  It is foreseeable that in the near future, LM-driven general AI agents will serve as essential tools in production tasks. This paper investigates scenarios involving the autonomous collaboration of future LM agents.
  arXiv  Detail & Related papers  (2024-09-22T14:09:49Z)
• Security of AI Agents [5.468745160706382]
  We identify and describe potential vulnerabilities in AI agents in detail from a system security perspective. We introduce defense mechanisms corresponding to each vulnerability with design and experiments to evaluate their viability. This paper contextualizes the security issues in the current development of AI agents and delineates methods to make AI agents safer and more reliable.
  arXiv  Detail & Related papers  (2024-06-12T23:16:45Z)
• AgentGym: Evolving Large Language Model-based Agents across Diverse Environments [116.97648507802926]
  Large language models (LLMs) are considered a promising foundation to build such agents. We take the first step towards building generally-capable LLM-based agents with self-evolution ability. We propose AgentGym, a new framework featuring a variety of environments and tasks for broad, real-time, uni-format, and concurrent agent exploration.
  arXiv  Detail & Related papers  (2024-06-06T15:15:41Z)
• KwaiAgents: Generalized Information-seeking Agent System with Large Language Models [33.59597020276034]
  Humans excel in critical thinking, planning, reflection, and harnessing available tools to interact with and interpret the world. Recent advancements in large language models (LLMs) suggest that machines might also possess the aforementioned human-like capabilities. We introduce KwaiAgents, a generalized information-seeking agent system based on LLMs.
  arXiv  Detail & Related papers  (2023-12-08T08:11:11Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• AGI Agent Safety by Iteratively Improving the Utility Function [0.0]
  We present an AGI safety layer that creates a special dedicated input terminal to support the iterative improvement of an AGI agent's utility function. We show ongoing work in mapping it to a Causal Influence Diagram (CID) We then present the design of a learning agent, a design that wraps the safety layer around either a known machine learning system, or a potential future AGI-level learning system.
  arXiv  Detail & Related papers  (2020-07-10T14:30:56Z)

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.