Watson: A Cognitive Observability Framework for the Reasoning of Foundation Model-Powered Agents

• URL: http://arxiv.org/abs/2411.03455v1
• Date: Tue, 05 Nov 2024 19:13:22 GMT
• Title: Watson: A Cognitive Observability Framework for the Reasoning of Foundation Model-Powered Agents
• Authors: Benjamin Rombaut, Sogol Masoumzadeh, Kirill Vasilevski, Dayi Lin, Ahmed E. Hassan,
• Abstract summary: 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.
• Score: 7.392058124132526
• License:
• Abstract: As foundation models (FMs) play an increasingly prominent role in complex software systems, such as FM-powered agentic software (i.e., Agentware), they introduce significant challenges for developers regarding observability. Unlike traditional software, agents operate autonomously, using extensive data and opaque implicit reasoning, making it difficult to observe and understand their behavior during runtime, especially when they take unexpected actions or encounter errors. In this paper, we highlight the limitations of traditional operational observability in the context of FM-powered software, and introduce cognitive observability as a new type of required observability that has emerged for such innovative systems. We then propose a novel framework that provides cognitive observability into the implicit reasoning processes of agents (a.k.a. reasoning observability), and demonstrate the effectiveness of our framework in boosting the debuggability of Agentware and, in turn, the abilities of an Agentware through a case study on AutoCodeRover, a cuttingedge Agentware for autonomous program improvement.

Related papers

• Interactive Agents to Overcome Ambiguity in Software Engineering [61.40183840499932]
  AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions. Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes. We study the ability of LLM agents to handle ambiguous instructions in interactive code generation settings by evaluating proprietary and open-weight models on their performance.
  arXiv  Detail & Related papers  (2025-02-18T17:12:26Z)
• AgentOps: Enabling Observability of LLM Agents [12.49728300301026]
  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.
  arXiv  Detail & Related papers  (2024-11-08T02:31:03Z)
• WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
  Large language models (LLMs) can mimic human-like intelligence. WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
  arXiv  Detail & Related papers  (2024-07-07T07:15:49Z)
• Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments [0.0]
  This thesis introduces an ontology-enhanced decision-making model (OntoDeM) for autonomous agents. OntoDeM enriches agents' domain knowledge, allowing them to interpret unforeseen events, generate or adapt goals, and make better decisions. Compared to traditional and advanced learning algorithms, OntoDeM shows superior performance in improving agents' observations and decision-making in dynamic, partially observable environments.
  arXiv  Detail & Related papers  (2024-05-27T22:52:23Z)
• Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning [23.763256908202496]
  Foundation models (FMs) have revolutionized the field of AI by showing remarkable performance in various tasks. FMs exhibit numerous limitations that prevent their broader adoption in many real-world systems. We propose a conceptual framework that encapsulates different modes by which agents could interact with FMs.
  arXiv  Detail & Related papers  (2024-02-02T18:00:35Z)
• Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement [65.26723285209853]
  We derive a framework to analyze whether a transparent implementation in a computing model is feasible. Based on previous results, we find that Blum-Shub-Smale Machines have the potential to establish trustworthy solvers for inverse problems.
  arXiv  Detail & Related papers  (2024-01-18T15:32:38Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Multi-Agent Verification and Control with Probabilistic Model Checking [4.56877715768796]
  Probabilistic model checking is a technique for formal automated reasoning about software or hardware systems. It builds upon ideas and techniques from a diverse range of fields, from logic, automata and graph theory, to optimisation, numerical methods and control. In recent years, probabilistic model checking has also been extended to integrate ideas from game theory.
  arXiv  Detail & Related papers  (2023-08-05T09:31:32Z)
• Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning [114.36124979578896]
  We design a dynamic mechanism using offline reinforcement learning algorithms. Our algorithm is based on the pessimism principle and only requires a mild assumption on the coverage of the offline data set.
  arXiv  Detail & Related papers  (2022-05-05T05:44:26Z)
• Differential Assessment of Black-Box AI Agents [29.98710357871698]
  We propose a novel approach to differentially assess black-box AI agents that have drifted from their previously known models. We leverage sparse observations of the drifted agent's current behavior and knowledge of its initial model to generate an active querying policy. Empirical evaluation shows that our approach is much more efficient than re-learning the agent model from scratch.
  arXiv  Detail & Related papers  (2022-03-24T17:48:58Z)
• Multi-Agent Reinforcement Learning with Temporal Logic Specifications [65.79056365594654]
  We study the problem of learning to satisfy temporal logic specifications with a group of agents in an unknown environment. We develop the first multi-agent reinforcement learning technique for temporal logic specifications. We provide correctness and convergence guarantees for our main algorithm.
  arXiv  Detail & Related papers  (2021-02-01T01:13:03Z)

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.