Debugging Claude Agents: Loops, Bad Tool Calls, Fixes (Harnessing Claudes Intelligence)

The first time a Claude agent works end-to-end in a demo, it feels like magic. The first time it spins in a loop for forty turns, burns through your token budget, and confidently calls a delete endpoint with an argument it invented, it feels like something else entirely. Agentic systems fail differently than ordinary software. There is no stack trace pointing at line 214. The model made a series of locally reasonable decisions that added up to a globally wrong outcome, and your job is to reconstruct why.

This post is a practitioner's guide to debugging Claude-based agents — the kind built on Claude Code, the Claude Agent SDK, or your own orchestration loop over the Anthropic API. We will walk through the three failure modes that account for the overwhelming majority of production incidents and the concrete techniques that resolve them.

Why agent bugs hide so well

A deterministic program fails the same way every time given the same input. A Claude agent does not. It samples from a probability distribution, so the same prompt can succeed nineteen times and fail the twentieth. That non-determinism is the root of most debugging pain: the bug you are chasing may not reproduce on the run where you finally turned on verbose logging.

The discipline that fixes this is total observability of the agent's trajectory. An agent trajectory is the full ordered sequence of model messages, tool calls, tool results, and intermediate reasoning across a single run. If you can replay a trajectory turn by turn — including the exact system prompt, the tool schemas as Claude saw them, and the raw JSON of every tool call — you can debug almost anything. If you only have the final answer, you are guessing. Log the trajectory before you do anything else.

Failure mode one: the infinite or near-infinite loop

Loops are the most expensive failure because they keep spending tokens until something stops them. They usually take one of three shapes. The first is the retry loop: a tool returns an error, Claude tries again with a near-identical call, gets the same error, and repeats. The second is the oscillation loop: the agent alternates between two states — reading a file, deciding it needs to edit, editing, re-reading, deciding it needs to revert — without converging. The third is the goal-drift loop: the agent keeps gathering more context because no step ever feels sufficient to act, so it researches forever.

flowchart TD
  A["Agent turn"] --> B{"Tool called?"}
  B -->|No| C["Final answer"]
  B -->|Yes| D["Execute tool"]
  D --> E{"Same call as a recent turn?"}
  E -->|Yes, 3rd repeat| F["Break: inject 'you are looping' note"]
  E -->|No| G{"Turn count > budget?"}
  G -->|Yes| H["Halt & escalate to human"]
  G -->|No| A
  F --> A

The fix has two layers. At the harness level, add a hard turn budget and a repetition detector: hash each tool call's name plus normalized arguments, and when the same hash appears three times in a sliding window, break the loop. Do not break silently — inject a message telling Claude it is repeating itself and asking it to either change approach or stop. Claude is good at recovering once it is told it is stuck; it simply cannot always notice on its own. At the prompt level, give the agent an explicit exit condition and permission to give up: "If a tool fails twice with the same error, stop and report the error rather than retrying."

Failure mode two: the wrong tool call

Wrong-tool errors come in two flavors. Either Claude picks a tool that exists but is inappropriate for the step, or it picks the right tool but at the wrong time. Both are usually caused by tool descriptions that are ambiguous, overlapping, or written for humans rather than for a model deciding under uncertainty.

Audit your tool schemas as if they were the only documentation Claude has — because they are. Two tools named search_records and query_database with similar descriptions will get confused constantly. Make each description say what the tool is for, when to use it, when not to use it, and what it returns. If you have ten tools and Claude keeps reaching for the wrong one, you almost always have too many tools or too little differentiation between them. Consolidating overlapping tools into one well-described tool often eliminates an entire class of misfires.

Timing errors — calling a write tool before reading current state, or calling a finalize tool before validation — are best fixed with ordering hints in the system prompt and, where it matters, with guardrails in the tool itself. A write tool that checks a precondition and returns a clear, actionable error ("You must call read_state first; current state is unknown") teaches Claude the correct order far more reliably than a prose instruction buried in the system prompt.

Failure mode three: hallucinated arguments

This is the scariest one because it can pass schema validation and still be wrong. Claude calls the correct tool with arguments that are syntactically valid but factually invented — a record ID it never actually retrieved, a date it assumed, a file path that does not exist. The model filled a required field because the schema demanded a value, and it produced a plausible one rather than admitting it did not know.

Three defenses work together. First, make your schemas honest: if an argument should only ever come from a previous tool result, say so in the field description ("Must be an ID returned by search_records; do not construct one"). Second, validate at the boundary. Before executing, check that ID-shaped arguments actually correspond to entities the agent has seen in this trajectory; if not, reject the call with an explanatory error so Claude can correct course. Third, prefer enums and constrained types over free strings wherever the value comes from a known set — a model cannot hallucinate a status that is not in the enum.

A repeatable debugging workflow

When an incident comes in, resist the urge to tweak the prompt immediately. Instead, pull the failing trajectory and read it like a transcript. Find the first turn where the agent's belief diverged from reality — not the turn where it crashed, but the earlier turn where it formed a wrong assumption. That divergence point is your real bug. Nine times out of ten the crash is several turns downstream of the actual mistake.

Once you find the divergence, ask which lever fixes it: a clearer tool description, a guardrail in the tool, a tightened schema, a system-prompt instruction, or a harness-level limit. Reach for prompt changes last, because they are the least durable — a prompt tweak that fixes one trajectory often regresses three others. Structural fixes in the tools and harness generalize. Finally, capture the failing trajectory as a regression test so the same divergence cannot silently return after your next change.

Frequently asked questions

How do I stop a Claude agent from looping forever?

Use defense in depth: a hard maximum turn count in your harness, a repetition detector that hashes tool calls and breaks when the same call repeats, and a prompt instruction giving the agent explicit permission to stop after a tool fails twice. When you break a loop, tell Claude it is looping rather than halting silently — it usually recovers immediately.

Why does Claude call the wrong tool?

Almost always because the tool descriptions overlap or are vague. Rewrite each schema to state precisely what the tool is for, when to use it, when not to, and what it returns. If you have many similar tools, consolidate them. Models choose tools from the descriptions alone, so the descriptions are the fix.

What is a hallucinated tool argument and how do I catch it?

It is a syntactically valid argument the model invented rather than retrieved — an ID, path, or date that does not exist. Catch it by validating arguments against entities the agent has actually seen in the current run, using enums for known-set values, and telling Claude in the field description never to construct certain values itself.

Should I debug by changing the prompt first?

No. Read the trajectory, find the first turn where the agent's belief diverged from reality, and prefer structural fixes — tool guardrails, schema constraints, harness limits — over prompt edits. Prompt tweaks are the least durable fix and often regress other cases.

Bringing reliable agents to your phone lines

CallSphere applies these same debugging disciplines to voice and chat — agents that answer every call, use tools mid-conversation, and recover gracefully instead of looping, so customers get a real answer 24/7. 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.