Agentic Metacognition: Designing a "Self-Aware" Low-Code Agent for Failure Prediction and Human Handoff

• URL: http://arxiv.org/abs/2509.19783v1
• Date: Wed, 24 Sep 2025 06:10:23 GMT
• Title: Agentic Metacognition: Designing a "Self-Aware" Low-Code Agent for Failure Prediction and Human Handoff
• Authors: Jiexi Xu,
• Abstract summary: Non-deterministic nature of autonomous agents presents reliability challenges.<n> secondary, "metacognitive" layer actively monitors primary LCNC agent.<n>Inspired by human introspection, this layer is designed to predict impending task failures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The inherent non-deterministic nature of autonomous agents, particularly within low-code/no-code (LCNC) environments, presents significant reliability challenges. Agents can become trapped in unforeseen loops, generate inaccurate outputs, or encounter unrecoverable failures, leading to user frustration and a breakdown of trust. This report proposes a novel architectural pattern to address these issues: the integration of a secondary, "metacognitive" layer that actively monitors the primary LCNC agent. Inspired by human introspection, this layer is designed to predict impending task failures based on a defined set of triggers, such as excessive latency or repetitive actions. Upon predicting a failure, the metacognitive agent proactively initiates a human handoff, providing the user with a clear summary of the agent's "thought process" and a detailed explanation of why it could not proceed. An empirical analysis of a prototype system demonstrates that this approach significantly increases the overall task success rate. However, this performance gain comes with a notable increase in computational overhead. The findings reframe human handoffs not as an admission of defeat but as a core design feature that enhances system resilience, improves user experience, and builds trust by providing transparency into the agent's internal state. The report discusses the practical and ethical implications of this approach and identifies key directions for future research.

Related papers

• When Benign Inputs Lead to Severe Harms: Eliciting Unsafe Unintended Behaviors of Computer-Use Agents [90.05202259420138]
  Unintended computer-use agents can deviate from expected outcomes even under benign input contexts.<n>We introduce the first conceptual and methodological framework for unintended CUA behaviors.<n>We propose AutoElicit: an agentic framework that iteratively perturbs benign instructions using CUA execution feedback.
  arXiv  Detail & Related papers  (2026-02-09T03:20:11Z)
• From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models [77.04403907729738]
  This survey charts the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior.<n>We demonstrate how uncertainty is leveraged as an active control signal across three frontiers.<n>This survey argues that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.
  arXiv  Detail & Related papers  (2026-01-22T06:21:31Z)
• The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution [63.61358761489141]
  Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering.<n>We propose a novel framework for textbfgeneral agentic attribution, designed to identify the internal factors driving agent actions regardless of the task outcome.<n>We validate our framework across a diverse suite of agentic scenarios, including standard tool use and subtle reliability risks like memory-induced bias.
  arXiv  Detail & Related papers  (2026-01-21T15:22:21Z)
• Gaming the Judge: Unfaithful Chain-of-Thought Can Undermine Agent Evaluation [76.5533899503582]
  Large language models (LLMs) are increasingly used as judges to evaluate agent performance.<n>We show this paradigm implicitly assumes that the agent's chain-of-thought (CoT) reasoning faithfully reflects both its internal reasoning and the underlying environment state.<n>We demonstrate that manipulated reasoning alone can inflate false positive rates of state-of-the-art VLM judges by up to 90% across 800 trajectories spanning diverse web tasks.
  arXiv  Detail & Related papers  (2026-01-21T06:07:43Z)
• From Biased Chatbots to Biased Agents: Examining Role Assignment Effects on LLM Agent Robustness [5.572574491501413]
  Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of actions with real-world impacts beyond text generation.<n>While persona-induced biases in text generation are well documented, their effects on agent task performance remain largely unexplored.<n>We present the first systematic case study showing that demographic-based persona assignments can alter LLM agents' behavior and degrade performance across diverse domains.
  arXiv  Detail & Related papers  (2026-01-21T02:43:07Z)
• Are Your Agents Upward Deceivers? [73.1073084327614]
  Large Language Model (LLM)-based agents are increasingly used as autonomous subordinates that carry out tasks for users.<n>This raises the question of whether they may also engage in deception, similar to how individuals in human organizations lie to superiors to create a good image or avoid punishment.<n>We observe and define agentic upward deception, a phenomenon in which an agent facing environmental constraints conceals its failure and performs actions that were not requested without reporting.
  arXiv  Detail & Related papers  (2025-12-04T14:47:05Z)
• Simulating and Understanding Deceptive Behaviors in Long-Horizon Interactions [18.182800471968132]
  We introduce the first simulation framework for probing and evaluating deception in large language models.<n>We conduct experiments across 11 frontier models, spanning both closed and open-source systems.<n>We find that deception is model-dependent, increases with event pressure, and consistently erodes supervisor trust.
  arXiv  Detail & Related papers  (2025-10-05T02:18:23Z)
• AgentCompass: Towards Reliable Evaluation of Agentic Workflows in Production [4.031479494871582]
  We present Agent, the first evaluation framework designed specifically for post-deployment monitoring and reasoning of agentic pipeline.<n>Agent achieves state-of-the-art results on key metrics, while uncovering critical issues missed in human annotations.
  arXiv  Detail & Related papers  (2025-09-18T05:59:04Z)
• An Empirical Study on Failures in Automated Issue Solving [12.571536148821144]
  We analyze the performance and efficiency of three SOTA tools, spanning both pipeline-based and agentic architectures, in automated issue solving tasks of SWE-Bench-Verified.<n>To move from high-level performance metrics to underlying cause analysis, we conducted a systematic manual analysis of 150 failed instances.<n>The results reveal distinct failure fingerprints between the two architectural paradigms, with the majority of agentic failures stemming from flawed reasoning and cognitive deadlocks.
  arXiv  Detail & Related papers  (2025-09-17T13:07:52Z)
• Profile-Aware Maneuvering: A Dynamic Multi-Agent System for Robust GAIA Problem Solving by AWorld [20.01452161733642]
  We propose a dynamic Multi-Agent System (MAS) in our AWorld framework.<n>An Execution Agent is supervised by a Guard Agent that provides on-demand dynamic maneuvering.<n>Our system achieves first place among open-source projects on the prestigious GAIA leaderboard.
  arXiv  Detail & Related papers  (2025-08-13T15:46:25Z)
• Criticality and Safety Margins for Reinforcement Learning [53.10194953873209]
  We seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users.<n>We introduce true criticality as the expected drop in reward when an agent deviates from its policy for n consecutive random actions.<n>We also introduce the concept of proxy criticality, a low-overhead metric that has a statistically monotonic relationship to true criticality.
  arXiv  Detail & Related papers  (2024-09-26T21:00:45Z)
• Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents [110.25679611755962]
  Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. We introduce Intention-in-Interaction (IN3), a novel benchmark designed to inspect users' implicit intentions through explicit queries. We empirically train Mistral-Interact, a powerful model that proactively assesses task vagueness, inquires user intentions, and refines them into actionable goals.
  arXiv  Detail & Related papers  (2024-02-14T14:36:30Z)
• How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation [46.42384207122049]
  We design SimulateBench to evaluate the believability of large language models (LLMs) when simulating human behaviors. Based on SimulateBench, we evaluate the performances of 10 widely used LLMs when simulating characters.
  arXiv  Detail & Related papers  (2023-12-28T16:51:11Z)
• Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines [4.987581730476023]
  Deep Neural Networks (DNN) are increasingly used as components of larger software systems that need to process complex data. Deep Learning based System (DLS) that implement a supervisor by means of uncertainty estimation.
  arXiv  Detail & Related papers  (2022-12-14T09:12:30Z)
• An Uncertainty-based Human-in-the-loop System for Industrial Tool Wear Analysis [68.8204255655161]
  We show that uncertainty measures based on Monte-Carlo dropout in the context of a human-in-the-loop system increase the system's transparency and performance. A simulation study demonstrates that the uncertainty-based human-in-the-loop system increases performance for different levels of human involvement.
  arXiv  Detail & Related papers  (2020-07-14T15:47:37Z)

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.