Debugging Claude Code: Loops, Bad Tool Calls, Fixes

The first time a Claude Code agent ran clean for me, I assumed I had built something reliable. The second run told a different story: it edited a file, re-read it, decided the edit hadn't taken, edited it again, and spiraled into a six-turn loop burning tokens on the same diff. Nothing crashed. There was no stack trace. The agent was simply confidently wrong about its own progress. That is the defining experience of debugging agentic systems — failures are rarely exceptions, they are behaviors. And behaviors need a different debugging mindset than code.

This post is about the three failure modes you will hit most with Claude Code and the Claude Agent SDK: runaway loops, wrong tool selection, and hallucinated tool arguments. For each I'll show how to see it, why it happens, and how to harden against it. The good news is that an HTML-and-text-native agent leaves a remarkably legible trail, and once you know what to look at, most of these failures become tractable.

Why agentic debugging is different

In traditional software a bug is a deterministic deviation: same input, same wrong output. With an agent, the same prompt can succeed nine times and fail the tenth because the model sampled a different path. Agent debugging is the practice of inspecting a run's full sequence of model decisions, tool calls, and tool results to find where the trajectory diverged from a good one. You are not debugging a line of code; you are debugging a decision.

That reframing matters because it changes your tooling. You stop reaching for a breakpoint and start reaching for a transcript. The single most valuable thing you can do before writing any clever guardrail is to log the complete interleaved stream — every assistant message, every tool name and input, every tool result — and read it like a detective reads a timeline. Ninety percent of the time the bug is obvious once you can actually see what the model saw.

Failure mode one: the loop

Loops are the most common and most expensive failure. They usually come from one of three sources. The model can't observe the effect of its action, so it repeats it. The tool returns ambiguous output that the model reads as failure. Or two tools fight each other — one writes state, another reports stale state. In my file-edit loop, the read tool was returning a cached view, so the agent never saw its own change land.

flowchart TD
  A["Agent takes action"] --> B{"Did result confirm progress?"}
  B -->|Yes| C["Advance to next step"]
  B -->|Ambiguous| D["Model re-tries same action"]
  D --> E{"Loop counter exceeded?"}
  E -->|No| A
  E -->|Yes| F["Break: summarize state & ask for help"]
  C --> G["Run continues"]
  F --> G

The structural fix is a loop budget. Track repeated tool calls with identical or near-identical arguments and trip a circuit breaker after N repeats — three is a sane default. When it trips, don't just abort; inject a system message that says, in effect, "You have attempted this action three times with the same result. Stop, summarize what you know, and either try a materially different approach or report that you are blocked." Claude models respond well to being told explicitly that a path is exhausted, because the loop usually persists only because nothing in the context signaled that repetition was happening.

The deeper fix is making actions observable. Ensure every tool returns a crisp, unambiguous signal of success or failure — a changed line count, a row ID, an explicit "no rows matched." Vague results like an empty string or a bare "OK" are loop fuel.

Failure mode two: the wrong tool call

When an agent has fifteen tools, it will sometimes pick the wrong one — calling a search tool when it should write, or a generic HTTP fetch when a purpose-built API tool exists. This is almost always a description problem, not a model problem. Tool descriptions are prompt engineering. If two tools have overlapping, vague descriptions, the model is guessing, and it will guess wrong under pressure.

Audit your tool definitions the way you would audit ambiguous function names in a shared codebase. Each description should state precisely what the tool does, when to use it, and — critically — when not to use it. "Use this to read files" is weak. "Read the current contents of a file by absolute path. Use this before editing. Do not use this to list directories; use list_dir instead" steers the model decisively. Reducing the tool count for a given subagent also helps enormously; an agent with three sharply-scoped tools makes better choices than one with twenty fuzzy ones.

Failure mode three: hallucinated arguments

Hallucinated arguments are when the model calls the right tool with invented inputs — a file path that doesn't exist, a column name it never saw, a customer ID it pattern-matched into existence. The cause is usually that the needed value was never actually in context, so the model filled the gap with something plausible. The fix is twofold: schema strictness and grounding.

On schema strictness, define tool input schemas tightly with enums, formats, and required fields, and validate before execution. When validation fails, return the error to the model with a specific message — "path must be absolute and start with /; you passed a relative path" — rather than throwing. The model can correct from a good error far better than it can from a silent failure. On grounding, make sure the values the agent needs are present in context before it needs them. If it must reference a database column, give it the schema first. An agent that hallucinates arguments is often an agent that was asked to act without the facts.

Building an observable run

All three fixes depend on observability, so invest there first. Instrument runs to emit a structured event per step: timestamp, step index, tool name, input hash, result summary, and token usage. Persist these. When a run misbehaves in production, you want to replay the exact trajectory, not guess. Hooks in Claude Code are a natural place to attach this logging without polluting your agent logic, and they let you enforce policy — block a dangerous tool call, redact a secret, or count repetitions — at the boundary.

One more practice pays off repeatedly: keep a small library of known-bad transcripts. Every time you debug a nasty loop or a wrong-tool incident, save the trajectory. Those become regression fixtures for your evals and a shared vocabulary for your team. "This is a type-three hallucination" is a far faster conversation than re-deriving the failure from scratch each time.

Frequently asked questions

How do I stop a Claude Code agent from looping?

Add a loop budget that counts repeated tool calls with the same arguments and breaks after about three, injecting a message that tells the agent the path is exhausted and it should try a different approach or report being blocked. Then fix the root cause by making tool results unambiguous about success or failure.

Why does my agent call the wrong tool?

Almost always because tool descriptions overlap or are vague. Rewrite each description to say exactly what it does, when to use it, and when not to, and reduce the number of tools a given subagent can see. Fewer, sharper tools beat many fuzzy ones.

What causes hallucinated tool arguments?

The model needed a value that was never actually in its context, so it invented a plausible one. Fix it by grounding — putting real schemas, paths, and IDs into context before the agent acts — and by validating inputs against strict schemas and returning specific, correctable error messages to the model.

Can I debug agent failures without rerunning them?

Yes, if you log the full interleaved trajectory of assistant messages, tool calls, and tool results per step. Replaying a stored transcript is faster and more reliable than re-triggering a nondeterministic run, and it lets you turn each incident into a regression fixture.

Bringing agentic AI to your phone lines

The same debugging discipline — observable trajectories, loop budgets, and grounded tool calls — is what keeps a live voice agent from talking in circles. CallSphere brings these agentic-AI patterns to voice and chat, with assistants that answer every call, use tools mid-conversation, and book work around the clock. See it live at callsphere.ai.

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Source & attribution: This is an independent, original explainer inspired by Anthropic's coverage on the Claude blog. Claude, Claude Code, Claude Cowork, Claude Opus, and the Model Context Protocol are products and trademarks of Anthropic. CallSphere is not affiliated with or endorsed by Anthropic.