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A coding-agent watchdog should live where the application can control the next step: in the host loop that sends model requests, runs tools, and returns their results. Set a finite run budget, watch for repeated actions and lack of verified progress, and decide in advance whether hitting a limit stops the run or asks a person to review it. The available details do not establish the programming language, thresholds, or test results for the system implied by the original title, so this guide explains a defensible design without claiming those specifics.
Where an agent loop happens
In a client-tool workflow, the model proposes a tool call; the host application executes it and sends the result back in another model request. The model does not itself execute tools controlled by the host. Anthropic’s tool-use documentation describes continuing while the response’s stop_reason is tool_use, with the application responsible for handling other stop reasons: How tool use works.
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That distinction determines where a reliable watchdog belongs. A prompt can ask an agent to stop, but a deterministic guard belongs in the orchestration code that decides whether to execute another tool-result cycle. Anthropic’s Claude Code team describes loops as cycles of work that continue until a stop condition is met, and treats choosing the loop and its stopping condition as an engineering problem: Loop engineering: Getting started with loops.
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Define completion before adding a limit
A watchdog cannot distinguish a productive long task from a stuck one unless the task has an observable goal. Before starting a run, specify what outcome counts as done and how it will be checked—for example, a test command that must pass or a requested file change that must be present. A 2026 preprint proposes capturing a loop’s trigger, goal, verification step, stopping rule, and memory as parts of a reusable loop specification: Stop Hand-Holding Your Coding Agent: Engineering the Loops that Replace Step-by-Step Prompting.
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Use the verification step as a real condition, not merely a success message from the agent. If a run reaches its budget without satisfying that condition, the result is incomplete or needs human review—not a completed task.
Use complementary signals, not one magic detector
No universal iteration or time threshold is established here. Choose limits for the tasks and costs your system can tolerate, then combine them with signals that indicate whether useful work is happening.
- Run budget: Cap total tool cycles, elapsed time, or both. This bounds the whole run, but time alone can mistake slow legitimate work for a loop.
- Repeated calls: Track tool identity and normalized arguments to detect consecutive equivalent actions. Repetition can be legitimate—polling or rerunning tests, for example—so treat it as a warning or combine it with other signals rather than blocking every repeat.
- Progress: Check for meaningful changes such as modified files, completed verification milestones, or informative tool output. A process can remain active while making no useful progress.
- Resource growth: Watch token use alongside progress. Rising usage without corresponding milestones may justify intervention, but does not by itself prove a loop.
A public Claude Code guide offers repeated actions, stalls, and token growth as monitoring signals, including an illustrative look at recent tool calls. Its example is not a validated universal threshold: Loop monitor agent.
Choose an enforcement point the harness controls
Guard the host loop
The orchestration layer can inspect each model response and decide whether to execute another tool call. Maintain run-level counters and progress state there. When a limit is reached, stop before starting another cycle and return a diagnostic that identifies the limit and the last relevant action. This approach applies to a host that owns the loop; exact implementation details depend on the agent framework.
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Use a supported blocking hook when appropriate
A platform hook may control an individual tool action rather than the whole run. Anthropic’s Claude Code guidance documents a PreToolUse hook that can inspect an impending call and deny it by exiting with code 2: Steering Claude Code: when to use CLAUDE.md, skills, hooks, and subagents. That is Claude Code-specific behavior, not a general feature of all coding agents; confirm that the platform’s hook runs before execution and can actually block the action.
Decide what happens when the watchdog trips
Intervention is part of the design, not an afterthought. Select a response that matches the risk of the task:
- Stop: End the run when a hard budget is exhausted or a clear repeated-action condition is met.
- Pause for review: Preserve the current state and ask a person to decide whether the repeated work is intentional.
- Allow one bounded retry: Retry only if the agent receives a changed strategy or useful new context; otherwise it may repeat the same failure. Keep the retry inside the original run budget.
Record the intervention reason, the signal that triggered it, and the relevant recent actions. That makes a stopped run explainable and helps distinguish a genuine loop from slow but productive work.
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What the evidence says about the risk
A 2026 preprint, When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents, reports 68 manually confirmed failures across 47 projects from 74 potential findings, and 91.9% precision for its analysis method: the study. These figures describe the paper’s repository analysis and manual review, not the share of all coding agents that loop. They support treating runaway repetition as a reliability issue without implying that every agent or workflow has the same risk.
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