Debugging Claude agents: loops, bad tool calls, hallucinated args

The first time a Claude agent you built spins forever, calling the same tool with the same arguments and getting the same error back, you stop trusting the system. You watch the trace scroll past - read file, read file, read file - and you realize the model has no idea it is stuck. Debugging agentic systems is its own discipline. It is not like debugging a function, where a stack trace points at one line. The failure lives in the loop between the model's reasoning and the tools it can reach, and you have to learn to read that loop the way you once learned to read a debugger.

This guide walks through the three failure modes that account for the overwhelming majority of broken Claude Cowork and Claude Agent SDK runs: infinite or oscillating loops, wrong tool calls, and hallucinated arguments. For each one we will look at what it actually looks like in a transcript, why the model produced it, and the concrete fixes that stop it from happening again.

Why agent debugging is different

A traditional program is deterministic: same input, same output, same bug every time. An agent is a probabilistic policy wrapped around tool execution. The same prompt can produce a clean run on Monday and a three-hundred-step disaster on Tuesday, because a slightly different sampling path led Claude down a branch where a tool returned something it did not expect. The bug is not in your code and not in the model - it is in the interaction, in the gap between what the tool said and what the model inferred from it.

That means the single most valuable thing you can build before you debug anything is a complete, structured trace. Every run should log the system prompt, the full message history, each tool call with its exact arguments, each tool result verbatim, and the model's reasoning text between steps. If you are running on the Claude Agent SDK, capture the raw request and response JSON, not a summarized version. Most agent debugging is just reading these traces slowly and asking, at each step, what did Claude believe was true here, and was it actually true?

An agent failure mode is a recurring pattern where the loop between model reasoning and tool execution stops making forward progress toward the goal. Naming the pattern is half the battle, because each named mode has a known set of causes and fixes.

Failure mode one: loops that never terminate

Loops come in two flavors. The first is the literal repeat: Claude calls search_docs with the identical query five times because each result was unhelpful and nothing in the context told it to try something different. The second is the oscillation: it edits a file, runs the test, the test fails, it reverts the edit, runs the test, it fails again, and it ping-pongs between two states forever. Both share a root cause - the agent cannot tell that it is repeating itself, because each step looks locally reasonable.

The diagram below traces how a loop forms and where each intervention breaks it.

flowchart TD
  A["Claude picks an action"] --> B["Tool runs, returns result"]
  B --> C{"Same action and result as a prior step?"}
  C -->|No| D["Real progress, continue"]
  C -->|Yes| E["Loop detector increments repeat count"]
  E --> F{"Repeat count over threshold?"}
  F -->|No| A
  F -->|Yes| G["Inject nudge or escalate to human"]
  G --> H["Force a different strategy or stop"]

The most effective fix is a loop detector that lives outside the model. Hash each tool-name-plus-normalized-arguments pair and keep a rolling window. When the same hash appears more than twice in a short span, do not just keep going - inject a system message that says, in effect, you have already tried this and it did not work; do not repeat it; either try a materially different approach or report that you are blocked. That single nudge resolves a large share of repeat loops, because Claude is perfectly capable of changing strategy once it is told the current one is exhausted.

For oscillation, the fix is usually structural rather than a nudge. Oscillation happens when the agent lacks a clear definition of done and treats each tool result as fresh evidence to reconsider. Give it an explicit success criterion it can check - the test command exits zero - and instruct it to stop the moment that criterion is met rather than continuing to tinker. A hard step budget is your backstop: cap any run at a sane number of steps and surface a clear failure instead of burning tokens forever.

Failure mode two: the wrong tool call

Wrong tool calls are subtler than loops because each one looks correct in isolation. Claude needs a customer's email, so it calls list_users when there is a perfectly good get_user_by_id. Or it calls a write tool when the task only needed a read. The agent is not malfunctioning; it is choosing the wrong instrument because the instruments were described badly or there are too many of them.

The dominant cause is a bloated, ambiguous tool surface. If you expose forty tools with overlapping names and vague one-line descriptions, the model has to guess. Tool selection accuracy degrades as the count climbs and as descriptions blur together. The fix starts in the tool definitions: write descriptions that say exactly when to use the tool and when not to, name parameters unambiguously, and prefer a few well-scoped tools over many near-duplicates. If two tools are easy to confuse, that confusion is a design defect, not a model defect.

The second cause is missing context about state. Claude calls a tool that fails because a precondition was not met - it tried to update a record that does not exist yet. The cure is to make tool results teach. When a tool fails, return a structured error that names the cause and suggests the next action, not a bare stack trace. A message like "user 4012 not found; call create_user first or verify the id with list_users" turns a dead end into a recovery path the model can actually follow.

Failure mode three: hallucinated arguments

Hallucinated arguments are the failure that scares people, because the call looks completely valid. Claude invokes refund_order with an order id and an amount - except that order id never appeared anywhere in the conversation. The model invented a plausible value to fill a required field. In a read-only tool this is annoying; in a tool that moves money or deletes data it is dangerous.

The structural defense is schema validation at the boundary, before the tool ever executes. Use strict JSON schemas with tight types, enums for closed sets, and format constraints, and reject any call whose arguments do not validate - returning the validation error to Claude so it can correct itself. A model is far less likely to keep hallucinating an id when the system keeps replying that the order id does not exist in this conversation and that it must use one that was actually retrieved.

The deeper fix is provenance. Require that high-stakes identifiers come from a prior tool result, not from the model's imagination. Before allowing a refund, check that the order id was returned by an earlier get_order call in this same run. If it was not, refuse and force a lookup. Pair that with a confirmation gate on any irreversible action: the agent proposes the call, a human or a deterministic policy approves it, and only then does it execute. Hallucinated arguments stop being catastrophic the moment the riskiest tools cannot fire on the model's word alone.

Building a debugging workflow that scales

One-off debugging does not scale; you need a repeatable loop. Start by turning every real failure into a saved transcript and, ideally, a regression test. When you find a loop or a hallucination, capture the exact message history that produced it and replay it after every prompt or tool change to confirm the fix held and nothing regressed.

Instrument aggressively in production. Log step counts, tool call distributions, error rates per tool, and the fraction of runs that hit the step cap. A tool with a high failure rate or a sudden spike in repeats is your next bug, surfaced before a user complains. Treat these metrics the way you treat latency and error dashboards for any service - agents are services, and they deserve the same observability.

Finally, fix causes, not symptoms. A nudge that breaks a specific loop is a patch; a clearer tool description, a tighter schema, or a real success criterion is a cure. The teams who ship reliable Claude agents are the ones who treat each failure as a signal about their tool surface and their prompts, and who keep grinding that surface down until the model rarely has a chance to go wrong in the first place.

Frequently asked questions

How do I stop a Claude agent from looping forever?

Combine three things: a hard step budget that ends any run after a sane number of steps, a loop detector that hashes tool-call signatures and nudges the model when it repeats, and an explicit success criterion so the agent knows when to stop. The step budget is your safety net; the detector and the criterion prevent most loops from forming.

Why does Claude call the wrong tool even when the right one exists?

Almost always because the tool surface is ambiguous: too many overlapping tools or vague descriptions. Rewrite each description to state exactly when to use the tool and when not to, prune near-duplicates, and return helpful structured errors so a wrong call teaches the model the right next move.

What is a hallucinated argument and how do I prevent it?

It is when the model fills a required parameter with a plausible but invented value, like an order id that never appeared in the conversation. Prevent it with strict schema validation, by requiring high-stakes identifiers to come from prior tool results, and by gating irreversible actions behind a confirmation step.

Do I need a special tool to debug agent runs?

You need complete structured traces more than you need a specific product. Capture the full message history, every tool call and verbatim result, and the model's reasoning between steps. Most agent bugs are found by reading that trace slowly and checking, at each step, what Claude believed versus what was actually true.

From traces to trustworthy phone lines

The same discipline that tames a Claude agent - loop detection, tight tool schemas, and provenance on risky actions - is exactly what keeps a voice assistant from going off the rails mid-call. CallSphere builds these guardrails into multi-agent voice and chat assistants that answer every call, use tools live in the conversation, and book work around the clock. See it in action at callsphere.ai.