Debugging Claude AI Agents: Loops, Bad Tool Calls, Fixes

The first time an agent works, it feels like magic. The fifth time it silently burns 40,000 tokens repeating the same failed file read, it feels like a haunted house. Debugging agentic systems is a different discipline from debugging ordinary code, because the bug is usually not in your code at all — it lives in the model's decision-making, in a tool description it misread, or in a stale piece of context it never let go of. This post is a practical guide to the failure modes you actually hit when building on Claude — Claude Code, the Claude Agent SDK, or a Model Context Protocol (MCP) toolchain — and how to find and fix them.

Why agent bugs hide where ordinary bugs don't

A normal program fails loudly: a stack trace, a non-zero exit code, a red test. An agent fails politely. It will confidently call search_orders("recent") when the tool expects an ISO date, get an empty result, apologize, and try again with "latest". Nothing crashed. The run just quietly produced garbage. The root cause is that the model is reasoning probabilistically over your tool schemas and the conversation so far, and any ambiguity in either becomes a coin flip that you only notice in aggregate.

The single most important debugging move, therefore, is to capture the full trace: every system prompt, every tool definition as the model actually saw it, every tool call with its raw arguments, every tool result, and every token of the model's interleaved reasoning. If you only log the final answer, you are debugging blind. With Claude's extended thinking and tool-use blocks, the model often tells you, in its own words, why it chose a tool — and that sentence is frequently where the bug is visible.

The four failure modes you will actually meet

Most agent misbehavior collapses into four recurring patterns. Infinite or near-infinite loops, where the agent retries the same action because the environment never gives it the signal it is waiting for. Wrong tool selection, where two tools have overlapping descriptions and the model picks the plausible-but-wrong one. Hallucinated arguments, where the model invents an ID, a path, or a parameter value it never observed. And premature completion, where the agent declares success without actually doing the work. Each has a distinct signature in the trace.

flowchart TD
  A["Agent run misbehaves"] --> B{"What does the trace show?"}
  B -->|Same call repeats| C["Loop: missing stop signal"]
  B -->|Empty or error results| D["Wrong tool or bad args"]
  B -->|Invented ID or path| E["Hallucinated argument"]
  B -->|Stops too early| F["Premature completion"]
  C --> G["Add loop guard & clearer tool result"]
  D --> H["Disambiguate tool descriptions"]
  E --> I["Constrain schema & require lookup first"]
  F --> J["Add explicit done-criteria check"]

Killing loops without papering over them

Loops almost always mean the agent cannot tell that it is done or that an action failed. Suppose a Claude agent calls read_file("config.yaml"), the file does not exist, and your tool returns an empty string. The model has no way to distinguish "empty file" from "file missing," so it tries variants forever. The fix is at the tool boundary, not the prompt: return a precise, structured error — {"error": "file_not_found", "path": "config.yaml", "hint": "call list_dir first"}. A good tool result is itself a debugging tool, because it steers the next decision.

You should still install a hard backstop. Track a fingerprint of each tool call — name plus normalized arguments — and if the identical fingerprint appears three times, break the loop and surface a synthetic message telling the model the action is not progressing and it should try a different approach or stop. In Claude Code this kind of guard rail is something you wire in a hook; with the Agent SDK it lives in your orchestration loop. The point is that no agent should be allowed to spend unbounded tokens without a circuit breaker. A loop guard is a small piece of orchestration code that detects repeated, non-progressing actions and forcibly interrupts the run.

Wrong tools and hallucinated arguments are usually a schema problem

When Claude reaches for the wrong tool, resist the urge to scold it in the system prompt. Look at the tool descriptions side by side as the model sees them. If you have get_customer and search_customers and both say "retrieve customer information," the model is guessing. Rewrite them to be mutually exclusive: get_customer — "Fetch exactly one customer by their known numeric ID. Fails if you do not have the ID." search_customers — "Find candidate customers by name or email when you do not yet know the ID." Disambiguation in the description fixes more wrong-tool bugs than any amount of prompt pleading.

Hallucinated arguments — an invented order number, a made-up file path — come from the model filling a required field it has no real value for. Two defenses compound well. First, make schemas strict and use enums and formats so an invalid value is rejected at the boundary and the error teaches the model. Second, enforce a workflow ordering: a tool that needs an ID should refuse to run until a search or list tool has actually returned that ID in the conversation. When the environment makes invention impossible, the model stops inventing. For genuinely free-form values, ask the model in the tool description to quote where it got the value, which surfaces fabrication in the trace.

Reproducing the bug so you can fix it once

Agents are stochastic, so a fix you cannot reproduce is a fix you cannot trust. Capture the exact inputs that produced the bad run — the user message, the tool set, the seed of context — and replay them. Pin the model version explicitly (for example Claude Sonnet 4.6 versus Opus 4.8), because a behavior you tuned on one model can shift on another. Where determinism matters for a regression test, drop the temperature and record the full request body so you can diff it later. The discipline that pays off most: turn every nasty production trace into a saved fixture, so the same failure never surprises you twice.

Context rot deserves its own mention. Long agent runs accumulate stale tool results, abandoned plans, and contradictory instructions, and the model starts weighting old information that no longer applies. If an agent suddenly references a file it edited twenty steps ago as if it were unchanged, suspect context, not reasoning. Compaction — summarizing and pruning the history between phases — often fixes "the agent got dumber over time" bugs that look mysterious until you read the full window.

Frequently asked questions

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

Almost always because the tool result does not clearly signal success, failure, or completion. The model retries because, from its point of view, nothing changed. Return structured, explicit results (including distinct errors), and add an orchestration-level loop guard that interrupts after a few identical, non-progressing calls.

How do I stop an agent from hallucinating IDs and file paths?

Make invention structurally impossible. Use strict schemas with enums and format validation so bad values are rejected at the boundary, and enforce ordering so an ID-consuming tool refuses to run until a search or list tool has surfaced a real value in the conversation.

What is the single most useful thing to log when debugging agents?

The complete trace: system prompt, the exact tool schemas the model saw, every tool call with raw arguments, every tool result, and the model's interleaved reasoning. Final-answer-only logging hides the decision where the bug lives.

My agent works on Sonnet but breaks on a different model — is that normal?

Yes. Tool-selection and argument behavior shift across models and even versions. Pin the model in your tests, treat a model swap as a change that needs re-evaluation, and keep saved fixtures so you can diff behavior across versions.

Bringing agentic AI to your phone lines

CallSphere puts these same debugging disciplines behind voice and chat agents — traced tool calls, loop guards, and strict schemas so the assistant that answers your phone uses the right tool and books real work. 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.