Debugging Claude Agent Skills: Loops, Bad Tool Calls

An agent that works perfectly in your first three test runs and then quietly burns forty tool calls trying to read a file that does not exist is a special kind of frustrating. It did not crash. It did not error. It just kept going, confidently, in the wrong direction. When you build agents with Claude Agent Skills, most of your hard debugging time is spent here: not on syntax errors, but on behavior that is plausible, expensive, and wrong. This post is a practical guide to the failure modes that actually show up in production and how to track each one to its root cause.

The mental shift that makes debugging tractable is this: a skill is not a function you called, it is a body of instructions the model chose to read and then interpreted. Every failure is either a discovery problem (the wrong skill loaded, or none did), an instruction problem (the skill said something ambiguous), or a tool problem (the skill's tools behaved unexpectedly and the model reacted badly). Almost every bug you will chase fits one of those three buckets.

Why agents fall into loops

The most common and most expensive failure is the loop: the agent calls a tool, dislikes the result, calls it again with a tiny variation, dislikes that, and repeats. Loops usually trace back to a tool that returns an unhelpful error. If a file-read tool returns the bare string Error with no path and no reason, the model has nothing to reason about, so it guesses. It tries a different filename, then a different directory, then the original again. From the model's point of view it is exploring; from your invoice's point of view it is hemorrhaging tokens.

The fix is almost always on the tool side, not the prompt side. Make every tool result self-explanatory: instead of Error, return File not found: /data/reports/q3.csv. Available files in /data/reports: q1.csv, q2.csv. That single change collapses most loops, because now the model can see the actual state of the world and correct in one step. The second defense is a hard stop: cap the number of turns a run may take, and have the skill instruct the model to summarize what it tried and ask for help once it has made two failed attempts at the same action.

Reading the trace like a flight recorder

You cannot debug what you cannot see. The single highest-leverage habit is logging the full conversation transcript — every tool call, every argument, every result — and reading it top to bottom when something goes wrong. The trace tells you exactly which skill loaded, what the model believed at each step, and the precise moment its model of the world diverged from reality. The diagram below shows the decision path I follow when triaging a bad run.

flowchart TD
  A["Bad agent run"] --> B{"Did the right skill load?"}
  B -->|No| C["Discovery bug: fix skill name/description"]
  B -->|Yes| D{"Were tool args correct?"}
  D -->|No| E["Hallucinated args: tighten schema & examples"]
  D -->|Yes| F{"Did the same call repeat?"}
  F -->|Yes| G["Loop: improve error messages, add turn cap"]
  F -->|No| H["Instruction bug: clarify the skill steps"]

Notice that the very first question is about discovery. A surprising share of "the agent is broken" reports are actually "the skill never loaded." Claude decides whether to read a skill based largely on its name and description, so a skill described as "helps with data" will lose to anything more specific. Read the trace, confirm the skill fired, and only then debug what it did.

Hallucinated arguments and how to starve them

Hallucinated arguments are the failure where the model invents a parameter the tool never advertised, or passes a customer ID it pattern-matched out of thin air. This is rarely the model being reckless; it is usually the model filling a gap your schema left open. If a tool accepts a region field but your description never says what values are legal, the model will cheerfully invent EU-WEST-7. Tight, enumerated schemas are the cure. Constrain to explicit enums where you can, mark required fields clearly, and give one concrete worked example inside the skill so the model has a correct template to imitate rather than a blank to improvise into.

A subtler variant is the stale argument: the model reuses a value from earlier in the conversation that is no longer valid. Defend against it by having tools echo the meaningful inputs back in their results — Updated order #4821 (status: shipped) — so the model re-grounds on each turn instead of trusting its own memory of three steps ago.

When the wrong tool gets called

Wrong-tool-call bugs come in two shapes. The first is overlap: two tools do similar things and the model picks the worse fit, like using a broad search tool when a precise lookup tool exists. The cure is to make tool descriptions disjoint and to say plainly when not to use each one. A description that reads "use this only for full-text search across documents; for exact ID lookups, use get_record instead" removes the ambiguity that caused the bad pick.

The second shape is sequencing: the model calls a write tool before the read tool that should have informed it, or skips a validation step the skill assumed was obvious. Skills should make order explicit. If a process has steps that must happen in sequence, number them and say so. Claude follows numbered, imperative steps far more reliably than it infers an implied order from prose.

Building a reproducible debugging loop

Ad hoc debugging does not scale past a handful of skills. The teams that ship reliable agents capture failing runs as fixtures: the exact starting prompt plus the environment state, saved so you can replay it. When a user reports a bad run, you reproduce it locally, watch it fail the same way, change one thing, and replay until it passes. That captured case then becomes a permanent regression test, which is the bridge from firefighting to a real evaluation suite.

One discipline pays for itself repeatedly: change one variable at a time. It is tempting to rewrite the skill, tighten three schemas, and add a turn cap all at once. Do that and you will never know which change fixed the bug, which means you cannot generalize the lesson. Isolate, replay, confirm, then move on.

It also helps to debug with the model itself as a collaborator. Paste the failing transcript back into a fresh session and ask Claude to explain, step by step, why it made the choice it did at the turn where things went wrong. The model is often startlingly clear about what misled it — an ambiguous instruction it read one way, a tool result it could not parse, a missing piece of context it tried to guess around. That self-explanation usually points straight at the line of the skill or the tool contract you need to fix, turning a baffling trace into a one-line edit.

Frequently asked questions

Why does my agent keep calling the same tool over and over?

Almost always because the tool's result does not give the model enough information to make progress. The model retries because retrying is the only move it can see. Fix the tool to return descriptive, actionable results — including what went wrong and what valid options exist — and add a turn cap so a stuck run fails fast instead of looping expensively.

How do I tell whether the skill even loaded?

Read the full run transcript. Skill loading is visible in the trace as the moment the model reads the skill's instructions. If you do not see it, the skill was not discovered, and your fix is the skill's name and description rather than its internal steps. A skill is a folder of instructions Claude loads on demand only when its description matches the task, so an unclear description is a silent failure.

What stops the model from inventing tool arguments?

Constrained schemas and concrete examples. Use enumerated values, mark required fields explicitly, and embed one correct example call inside the skill. When the model has a precise template and a closed set of legal values, it has far less room to improvise an argument that does not exist.

Should I debug in the prompt or in the tools?

Start with the tools. A large share of agent misbehavior is downstream of opaque tool results and loose schemas. Make tools self-explanatory and strict first; you will find that many prompt-level symptoms disappear once the model can actually see what is happening.

Bringing agentic AI to your phone lines

CallSphere applies these same debugging-first agentic patterns to voice and chat — multi-agent assistants that answer every call and message, call tools mid-conversation, and recover gracefully when something goes sideways. See it live at callsphere.ai.

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.