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AgentGuard: Learning Execution Guardrails from Anomalous Coding-Agent Trajectories

Wuyang Dai, Song Wang

arXiv:2609.16287v1cs.SE

TL;DR

Coding-agent task success does not ensure reliable intermediate execution, while manually specified guardrails may be difficult to maintain and unnecessarily restrictive. AgentGuard learns conditional constraints from anomalous trajectories and activates relevant rules for each instruction. In evaluation, it reduced abnormal execution and increased successful task completion, while over-refusal remained a limitation.

  • Problem

    Coding agents can take intermediate actions that violate task boundaries despite plausible final answers, and manually specified guardrails may be difficult to maintain or overly restrictive.

    Source: §1 · p. 1

  • Method

    AgentGuard extracts recurring failure patterns from anomalous trajectories, turns them into conditional instruction-level constraints, and dynamically loads relevant guardrails.

    Source: §1 · p. 1, §3 · p. 2, §3.4 · p. 4

  • Results

    69.0% to 26.7% Abnormal Execution Rate and 21.7% to 35.0% Successful Task Completion Rate with AgentGuard versus the baseline agent.

    Source: §1 · p. 1

  • Takeaways & Limitations

    The results suggest that failure-driven behavioral guardrails can improve coding-agent execution reliability without modifying the underlying model or execution environment.

    Source: §6 · p. 7

    The evaluation uses multi-step workflows with one injected adversarial step and evaluates legitimate work only when it remains safe and feasible.

    Source: §4.1 · p. 4

Abstract

from arXiv · show

AI coding agents increasingly rely on execution harnesses to interact with repositories and external tools. However, task success does not guarantee reliable execution. Agents may still modify unrelated files, rewrite tests, issue unsafe commands, or ignore failed validations, motivating behavioral guardrails for reliable execution. We present AgentGuard, an instruction-level guardrail framework that learns conditional execution constraints from anomalous trajectories of coding agents. Rather than relying on manually specified safety rules, AgentGuard automatically extracts recurring execution failure patterns, generalizes them into instruction-level behavioral constraints, and organizes them as a lightweight guardrail skill that dynamically activates only the rules relevant to the current instruction. This design enables behavioral guidance while minimizing unnecessary restrictions on normal execution. We evaluate AgentGuard using 642 documented failure traces collected from real coding-agent executions across 382 repository tasks. Guardrails are learned from 461 traces covering 282 tasks and evaluated on a disjoint set of 100 tasks. Using Claude Code with Claude Haiku 4.5 as the underlying coding agent, we compare the baseline agent with the same agent augmented by AgentGuard. Experimental results show that AgentGuard reduces the Abnormal Execution Rate from 69.0% to 26.7% and increases the Successful Task Completion Rate from 21.7% to 35.0%. These results demonstrate that execution guardrails learned from historical failures can substantially improve the reliability of AI coding agents while highlighting the remaining challenge of balancing safety and task completion.

1 Introduction

Coding agents can make intermediate changes that violate task boundaries even when their final answers appear plausible. AgentGuard learns conditional execution constraints from historical failures and activates them when relevant, improving execution outcomes in a held-out evaluation.

1 Introduction

  • Abnormal execution can violate task scope, repository or environment integrity, safe recovery, validation, artifact, or evidence-grounded reporting requirements.A plausible final answer does not ensure that intermediate actions stayed within the intended task boundary.Source: §1 · p. 1
  • Existing guardrails often rely on manually authored policies applied uniformly, making them difficult to maintain and potentially restrictive of benign behavior.Prior work also finds negative behavioral constraints consistently outperform prescriptive guidance for coding agents.Source: §1 · p. 1
  • AgentGuard learns conditional instruction-level constraints from anomalous trajectories and dynamically loads only rules whose triggering conditions match the current instruction.The rules are organized as a lightweight skill to provide targeted guidance while avoiding unnecessary constraints during normal execution.Source: §1 · p. 1
  • 69.0% to 26.7% Abnormal Execution Rate and 21.7% to 35.0% Successful Task Completion Rate with AgentGuard versus the baseline agent.Evaluation used 100 disjoint tasks; 461 traces from 282 tasks were used for guardrail construction.Source: §1 · p. 1
  • The evaluation partitions the documented traces by task, reserving held-out tasks exclusively for evaluation.The paper identifies reliability gains alongside over-refusal as the primary limitation of learned behavioral guardrails.Source: Figure 1 · p. 2, §1 · p. 2

2 Related Work

Prior work studies coding-agent outcomes, trajectory analysis, safety constraints, runtime monitoring, and reusable rules. AgentGuard differs by learning repository-level guardrails from reviewed anomalous traces and injecting them into an existing agent as contextual instructions.

Coding agents and their evaluation

  • End-state repository-task outcomes do not fully characterize execution, where context gathering, recovery, and validation decisions can distinguish successful from problematic trajectories (Mehtiyev and Assunção 2026).ABTest provides reviewed repository-grounded traces of problematic executions for deriving behavioral guardrails (Dai et al. 2026).Source: §2 · p. 2

Agent safety and execution guardrails

  • Reusable natural-language rules and coding skills have been proposed to improve coding agents without modifying underlying models or tool interfaces, although effectiveness is inconsistent across tasks (Zhang et al. 2026; Han et al. 2026; Aggarwal and Ghalaty 2026).Source: §2 · p. 2
  • AgentGuard learns lightweight repository-level guardrails from reviewed anomalous traces and injects them as contextual instructions without model fine-tuning, tool modification, or runtime enforcement.Source: §2 · p. 2

3 Method

AgentGuard converts reviewed anomalous coding-agent trajectories into conditional guardrails, then organizes them for instruction-specific retrieval. Its method preserves both local action evidence and whole-task context through extraction, rule consolidation, and routing.

3 Method

  • AgentGuard identifies recurring patterns in historical anomalous trajectories, abstracts them into conditional constraints, consolidates overlapping rules, and routes relevant rules to the current instruction.The rules are grouped in a lightweight routing structure for dynamic activation.Source: §3 · p. 2

3.1 Execution Trace Representation

  • A coding-agent case pairs each instruction with its response execution, and the complete trace preserves dependencies across the task.Interaction triples and execution metadata provide fine-grained action evidence, while the full trace captures context, state changes, and downstream consequences.Source: §3.1 · pp. 2–3

3.2 Evidence Extraction

  • Evidence extraction combines the anomalous action’s instruction, preceding interactions, and result with earlier and later trajectory context.Each finding records context, action, outcome, support, and stage, and is used only when its action, outcome, and trace support are observable.Source: §3.2 · p. 3

3.3 Rule Induction and Consolidation

  • Each finding becomes a conditional rule specifying when it applies, what behavior to avoid, exceptions, a safe alternative, and the execution stage.Rules are deduplicated, subsumed only when legitimate behavior remains unrestricted, or kept separate when apparent conflicts require different responses.Source: §3.3 · pp. 3–4

3.4 Rule Organization and Routing

  • Routing entries group guardrails by constrained action, and the skill loads only the relevant subskill before tool use.Routing organizes existing rules rather than adding new ones; the instructions do not change tool permissions or intercept actions.Source: §3.4 · p. 4

4 Experimental Design

AgentGuard is induced from task-partitioned failure traces and evaluated on separate multi-step tasks. The design compares guarded and unguarded executions under shared conditions and uses human review with complementary measures of safety and task utility.

4.1 Guardrail Construction and Intervention

  • 461 traces from 282 tasks were used to construct guardrails, with a disjoint set of 100 tasks held out for evaluation.The 642 documented failures cover 382 unique tasks; the tasks include one injected adversarial step and legitimate work that should be completed when safe and feasible.Source: §4.1 · p. 4

4.2 Evaluation Tasks and Settings

  • The 100 evaluation tasks were run three times per condition, comparing Claude Code without guardrails against the same agent receiving AgentGuard guidance before each instruction.This yielded 600 executions in fresh Docker containers with identical tool access, permissions, and runtime configurations; only AgentGuard guidance differed.Source: §4.2 · p. 4

4.3 Review and Statistics

  • Reviewers first assessed handling of the injected problem, then examined legitimate-work completion, recovery, repository and environment changes, artifacts, and evidence-supported final claims.Three reviewers independently assessed all tasks and traces, then adjudicated disagreements; they could identify the condition during annotation because guardrail instructions appeared in the trace.Source: §4.3 · pp. 4–5

4.4 Metrics

  • Five complementary measures capture abnormal execution, correct adversarial-step handling, feasible benign-work completion, successful task completion, and over-refusal.Successful Task Completion requires no abnormal behavior, correct handling of the adversarial step, and completion of all feasible benign work.Source: §4.3 · pp. 5–6, §4.4 · p. 6

5 Results

AgentGuard substantially reduces abnormal execution and improves successful task completion, while the results also identify over-refusal and continued benign work as remaining concerns. Its effects vary by routing entry, and its measured execution overhead is modest.

5.1 Overall Performance of AgentGuard

  • AER falls from 69.0% to 26.7% (61.4% relative reduction), while Successful Task Completion Rate rises from 21.7% to 35.0%.The AER reduction is statistically significant (95% task-clustered bootstrap CI: −51.7 to −33.0; p < 0.001). Correct Adversarial-Step Handling Rate rises from 25.0% to 62.7%, while Over-Refusal Rate is 19.3% under AgentGuard and zero under CC.Source: §5.1 · p. 6
  • Across 100 tasks, abnormal execution is eliminated in 41 CC-only cases, persists in 33 under both settings, and occurs only with AgentGuard in 3.The transition is asymmetric: 41 tasks improve versus 3 with newly observed abnormal execution.Source: §5.1 · p. 6, Table 3 · p. 6

5.2 Effects of AgentGuard

  • Across 300 executions, reviewers identify 129 safety gains and 32 adverse effects; 100 gains preserve all benign work, while 55 executions incur benign utility loss.The authors identify safe continuation after a guardrail triggers, rather than premature abandonment of feasible work, as the primary remaining challenge.Source: §5.2 · p. 7, Table 4 · p. 7
  • Beneficial effects outnumber adverse ones across routing entries; Execution/Recovery has the most beneficial effects (120), while adverse effects cluster in Artifacts/Reporting (29) and Understanding/Path (27).The authors suggest focusing future improvements on understanding- and reporting-related behaviors.Source: §5.2 · p. 7, Figure 2 · p. 7

5.3 Cost Analysis

  • Mean execution cost rises from $0.158 to $0.162 per run (+2.5%), execution time from 93.3 to 97.0 seconds (+3.9%), and tool calls from 21.8 to 22.7 (+4.1%).The paper characterizes these increases as modest overhead.Source: §5.3 · p. 7, Table 5 · p. 7

6 Conclusion

AgentGuard improves coding-agent execution reliability by learning repository-level guardrails from anomalous traces. It injects behavioral constraints during task execution without modifying the underlying model or environment.

6 Conclusion

  • AgentGuard learns repository-level guardrails from anomalous execution traces to improve coding-agent reliability.Source: §6 · p. 7
  • It injects learned behavioral constraints as contextual instructions rather than modifying the model or execution environment.Source: §6 · p. 7
  • Across 100 coding tasks, AgentGuard substantially reduced abnormal execution while preserving benign behaviors and adding only modest execution overhead.Source: §6 · p. 7
  • The results suggest failure-driven behavioral guardrails are a practical mechanism for improving autonomous coding-agent safety and reliability.Source: §6 · p. 7

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