CodeTracer: Towards Traceable Agent States

• URL: http://arxiv.org/abs/2604.11641v3
• Date: Wed, 15 Apr 2026 14:20:56 GMT
• Title: CodeTracer: Towards Traceable Agent States
• Abstract summary: We present CodeTracer, a tracing architecture that parses heterogeneous run artifacts through evolving extractors.<n>CodeTracer reconstructs the full state transition history as a hierarchical trace tree with persistent memory.<n>It performs failure onset localization to pinpoint the failure origin and its downstream chain.
• Score: 40.51936201889185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code agents are advancing rapidly, but debugging them is becoming increasingly difficult. As frameworks orchestrate parallel tool calls and multi-stage workflows over complex tasks, making the agent's state transitions and error propagation hard to observe. In these runs, an early misstep can trap the agent in unproductive loops or even cascade into fundamental errors, forming hidden error chains that make it hard to tell when the agent goes off track and why. Existing agent tracing analyses either focus on simple interaction or rely on small-scale manual inspection, which limits their scalability and usefulness for real coding workflows. We present CodeTracer, a tracing architecture that parses heterogeneous run artifacts through evolving extractors, reconstructs the full state transition history as a hierarchical trace tree with persistent memory, and performs failure onset localization to pinpoint the failure origin and its downstream chain. To enable systematic evaluation, we construct CodeTraceBench from a large collection of executed trajectories generated by four widely used code agent frameworks on diverse code tasks (e.g., bug fixing, refactoring, and terminal interaction), with supervision at both the stage and step levels for failure localization. Experiments show that CodeTracer substantially outperforms direct prompting and lightweight baselines, and that replaying its diagnostic signals consistently recovers originally failed runs under matched budgets. Our code and data are publicly available.

Related papers

• Skynet: Workflow-Level Anomaly Detection for Agentic AI via Semantic and Structural Modeling [31.732010254379052]
  Agentic AI systems execute complex tasks through planning, tool use, and multi-agent coordination.<n>Existing defenses either target a specific class of attacks or failures, or inspect individual prompts and steps in isolation.<n>We present Skynet, a principled anomaly detection framework that turns observed multi-agent execution into directed workflow graphs.
  arXiv  Detail & Related papers  (2026-09-06T21:16:42Z)
• TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories [48.638067300108276]
  Critical error detection aims to locate the earliest error step in a failed trajectory that is responsible for the final failure.<n>Long trajectories make it difficult to identify individual errors, since the evidence for judging a step may be scattered across distant instructions, observations, and prior context.<n>We propose Traj Debug, an error-lifecycle tracing framework that addresses long-trajectory error discovery.
  arXiv  Detail & Related papers  (2026-08-06T17:51:20Z)
• Meta-Agent: From Task Descriptions to Verified Multi-Agent Systems [36.485119937780524]
  We present Meta-Agent, a two-phase framework that automatically constructs and executes specialized multi-agent systems.<n>We introduce a three-level error attribution mechanism that distinguishes local, upstream, and structural failures.<n>Experiments against strong multi-agent baselines and ablation studies demonstrate consistent improvements in task success rate, error recovery, and workflow stability.
  arXiv  Detail & Related papers  (2026-05-24T19:38:54Z)
• Trace2Skill: Verifier-Guided Skill Evolution for Long-Context EDA Agents [0.3733676450456031]
  We present Trace2Skill, a test-time scaling framework that improves a hardware agent without RTL-specialized model fine-tuning.<n>Rather than training a new model or only sampling more candidate solutions, Trace2Skill treats the agent's natural-language skill as an evolvable policy.<n>It mines repeated rollout traces for success and failure modes, converts them into dense diagnostics and oracle lessons, and uses an oracle, mutator, and selector loop to produce task-specific skills.
  arXiv  Detail & Related papers  (2026-05-20T23:10:49Z)
• Step-level Optimization for Efficient Computer-use Agents [51.29573359027217]
  We argue that strong computer-use agents remain expensive and slow in practice.<n>We propose an event-driven, step-level cascade for computer-use agents.
  arXiv  Detail & Related papers  (2026-04-29T19:59:36Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• AgentTrace: Causal Graph Tracing for Root Cause Analysis in Deployed Multi-Agent Systems [0.0]
  We present AgentTrace, a lightweight causal tracing framework for post-hoc failure diagnosis in deployed multi-agent AI systems.<n>AgentTrace reconstructs causal graphs from execution logs, traces backward from error manifestations, and ranks candidate root causes using interpretable structural and positional signals.<n>Our results suggest that causal tracing provides a practical foundation for improving the reliability and trustworthiness of agentic systems in the wild.
  arXiv  Detail & Related papers  (2026-03-16T00:46:44Z)
• AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents [50.481033105867205]
  We introduce AgentProcessBench, the first benchmark dedicated to evaluating step-level effectiveness in realistic, tool-augmented trajectories.<n>The benchmark comprises 1,000 diverse trajectories and 8,509 human-labeled step annotations with 89.1% inter-annotator agreement.<n>It features a ternary labeling scheme to capture exploration and an error propagation rule to reduce labeling ambiguity.
  arXiv  Detail & Related papers  (2026-03-15T16:13:58Z)
• ANCHOR: Branch-Point Data Generation for GUI Agents [52.22377425487]
  End-to-end GUI agents for real desktop environments require large amounts of high-quality interaction data.<n>We present a trajectory expansion framework Anchor that bootstraps scalable desktop supervision from a small set of verified seed demonstrations.<n>Experiments on standard desktop benchmarks, OSWorld and WindowsAgentArena, show that models fine-tuned on our expanded corpus achieve consistent improvements.
  arXiv  Detail & Related papers  (2026-02-06T19:55:26Z)
• TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code [11.207330722400764]
  We present TraceCoder, a framework that emulates the observe-analyze-repair process of human experts.<n>The framework first instruments the code with diagnostic probes to capture fine-grained runtime traces.<n>It then conducts causal analysis on these traces to accurately identify the root cause of the failure.
  arXiv  Detail & Related papers  (2026-02-06T16:59:48Z)
• AgentRx: Diagnosing AI Agent Failures from Execution Trajectories [9.61742219198197]
  We release a benchmark of 115 failed trajectories spanning structured API, incident management, and open-ended web/file tasks.<n>Each trajectory is annotated with a critical failure step and a category from a grounded-theory derived, cross-domain failure taxonomy.<n>We present AGENTRX, an automated domain-agnostic diagnostic framework that pinpoints the critical failure step in a failed agent trajectory.
  arXiv  Detail & Related papers  (2026-02-02T18:54:07Z)
• InspectCoder: Dynamic Analysis-Enabled Self Repair through interactive LLM-Debugger Collaboration [71.18377595277018]
  Large Language Models (LLMs) frequently generate buggy code with complex logic errors that are challenging to diagnose.<n>We present InspectCoder, the first agentic program repair system that empowers LLMs to actively conduct dynamic analysis via interactive debugger control.
  arXiv  Detail & Related papers  (2025-10-21T06:26:29Z)
• A Unified Debugging Approach via LLM-Based Multi-Agent Synergy [39.11825182386288]
  FixAgent is an end-to-end framework for unified debug through multi-agent synergy. It significantly outperforms state-of-the-art repair methods, fixing 1.25$times$ to 2.56$times$ bugs on the repo-level benchmark, Defects4J.
  arXiv  Detail & Related papers  (2024-04-26T04:55:35Z)

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.