A Taxonomy of AgentOps for Enabling Observability of Foundation Model based Agents

• URL: http://arxiv.org/abs/2411.05285v1
• Date: Fri, 08 Nov 2024 02:31:03 GMT
• Title: A Taxonomy of AgentOps for Enabling Observability of Foundation Model based Agents
• Authors: Liming Dong, Qinghua Lu, Liming Zhu,
• Abstract summary: LLMs have fueled the growth of a diverse range of downstream tasks, leading to an increased demand for AI automation. As AI agent systems tackle more complex tasks and evolve, they involve a wider range of stakeholders. These systems integrate multiple components such as AI agent, RAG pipelines, prompt management, agent capabilities, and observability features. It is essential to shift towards designing AgentOps platforms that ensure observability and traceability across the entire development-to-production life-cycle.
• Score: 12.49728300301026
• License:
• Abstract: The ever-improving quality of LLMs has fueled the growth of a diverse range of downstream tasks, leading to an increased demand for AI automation and a burgeoning interest in developing foundation model (FM)-based autonomous agents. As AI agent systems tackle more complex tasks and evolve, they involve a wider range of stakeholders, including agent users, agentic system developers and deployers, and AI model developers. These systems also integrate multiple components such as AI agent workflows, RAG pipelines, prompt management, agent capabilities, and observability features. In this case, obtaining reliable outputs and answers from these agents remains challenging, necessitating a dependable execution process and end-to-end observability solutions. To build reliable AI agents and LLM applications, it is essential to shift towards designing AgentOps platforms that ensure observability and traceability across the entire development-to-production life-cycle. To this end, we conducted a rapid review and identified relevant AgentOps tools from the agentic ecosystem. Based on this review, we provide an overview of the essential features of AgentOps and propose a comprehensive overview of observability data/traceable artifacts across the agent production life-cycle. Our findings provide a systematic overview of the current AgentOps landscape, emphasizing the critical role of observability/traceability in enhancing the reliability of autonomous agent systems.

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.