Debugging Claude Agents: Loops, Bad Tool Calls, Bad Args (Cowork Enterprise Ready)

The first time a Claude agent goes into an infinite loop in front of a real user, you stop thinking of agents as magic and start thinking of them as distributed systems with a very chatty failure mode. An agent that calls the same search tool eleven times, each time tweaking one word in the query, is not "thinking harder" — it is stuck, and it is burning your token budget while it does. Debugging agentic systems is its own discipline, and most of the hard-won lessons are about reading the transcript, not reading the model weights.

This post is a practical guide to the three failure modes that account for the overwhelming majority of broken agent runs when you build on Claude Code or the Claude Agent SDK: loops, wrong tool calls, and hallucinated arguments. For each one I'll show how to recognize it in a trace, why it happens, and the concrete guardrail that stops it.

Key takeaways

• Most agent bugs are visible in the transcript — instrument every tool call with inputs, outputs, latency, and a turn index before you debug anything.
• Loops come from missing progress signals; break them with a turn budget, a repeat-call detector, and a forced "reflect or stop" turn.
• Wrong tool selection is usually a tool-description problem, not a model problem — sharpen names and "when to use" lines first.
• Hallucinated arguments are a schema problem; tight JSON Schema with enums and required fields converts a silent failure into a catchable validation error.
• A deterministic replay harness lets you reproduce a flaky run and confirm a fix without paying for live calls each time.

Why agent bugs hide where logs don't look

A traditional service fails loudly: an exception, a 500, a stack trace. An agent fails politely. It produces fluent text and a plausible tool call, and the only sign that anything is wrong is that the answer is subtly off or the run took forty seconds and nine tool calls to do a two-call job. The defect lives in the sequence of decisions, not in any single line of code.

That means your first investment in debuggability is not a clever prompt — it's a structured trace. For every turn, log the turn index, the tool name requested, the full input arguments, the raw tool result, the latency, and the token counts. Claude Code and the Agent SDK expose these as structured events; capture them to a store you can query. When you can run a query like "show me every run where the same tool was called more than five times," you have turned an invisible class of bugs into a dashboard.

Failure mode one: loops that never converge

A loop is what you get when the agent has no reliable signal that it is making progress. It searches, gets a thin result, re-searches with a slightly different phrasing, gets another thin result, and repeats — because nothing in the loop tells it that the strategy is failing and a different action is needed.

flowchart TD
  A["Agent turn"] --> B{"Tool call requested?"}
  B -->|No| C["Return final answer"]
  B -->|Yes| D{"Turn budget exceeded?"}
  D -->|Yes| E["Force stop & summarize state"]
  D -->|No| F{"Same call seen 3x?"}
  F -->|Yes| G["Inject reflect prompt: change strategy"]
  F -->|No| H["Execute tool"]
  G --> H
  H --> A

The diagram shows the three controls that, together, kill almost every loop. First, a hard turn budget — the orchestrator counts tool-using turns and force-stops past a ceiling, returning whatever partial state exists rather than spinning forever. Second, a repeat-call detector that hashes the tool name plus normalized arguments and notices when the same call recurs. Third, when a repeat is detected, you don't just kill the run — you inject a short system message that tells Claude the current approach is not working and asks it to change strategy or report that the task can't be completed. That reflection turn breaks far more loops than a blunt kill switch, because the model usually does know an alternative once it's told the current path is dead.

def guard_turn(history, max_turns=12):
    if len([t for t in history if t.tool_call]) >= max_turns:
        return "STOP: turn budget reached. Summarize what you found."
    recent = [normalize(t) for t in history[-6:] if t.tool_call]
    if recent and recent.count(recent[-1]) >= 3:
        return ("You have repeated the same tool call 3 times with no new "
                "information. Change your approach or say you cannot proceed.")
    return None  # let the turn run

This helper runs before each turn. If it returns a string, you append it as a system note and let the model respond to it; if it returns None, the turn proceeds normally. It is maybe twelve lines, and it is the single highest-leverage piece of debugging code you will write.

Failure mode two: the wrong tool for the job

When an agent picks the wrong tool — calling a generic web search when you have a precise internal lookup, or reaching for a file write when it should have read first — the instinct is to blame the model. Resist it. Nine times out of ten the problem is that two tools have overlapping or vague descriptions, and Claude is choosing reasonably given ambiguous instructions.

Fix the tool surface before you touch the prompt. Give each tool a name that states its job, a one-line description that begins with a clear "Use this when…", and an explicit note about when NOT to use it if it's commonly confused with a sibling. The model reads these descriptions the way a new engineer reads a function's docstring; if your docstring is vague, expect vague behavior. Reducing the number of tools available in a given context also helps — an agent with six well-scoped tools chooses more reliably than one with twenty-five overlapping ones.

Failure mode three: hallucinated arguments

The most insidious failure is the well-formed tool call with a wrong argument: a made-up customer ID, a date in the wrong format, an enum value that doesn't exist. The call looks valid, the tool may even half-succeed, and the error surfaces three steps downstream where it's hard to trace back.

The defense is strict input schemas. Define each tool's parameters with JSON Schema, mark required fields, constrain free-form strings to enums where you can, and validate every argument before execution. When validation fails, don't crash — return the validation error back to the model as the tool result so it can correct itself.

{
  "name": "refund_order",
  "description": "Issue a refund. Use ONLY after confirming the order exists via lookup_order.",
  "input_schema": {
    "type": "object",
    "properties": {
      "order_id": {"type": "string", "pattern": "^ord_[0-9]{8}$"},
      "reason": {"type": "string", "enum": ["damaged", "late", "wrong_item", "other"]},
      "amount_cents": {"type": "integer", "minimum": 1}
    },
    "required": ["order_id", "reason", "amount_cents"]
  }
}

The pattern on order_id turns a hallucinated ID into an immediate, catchable validation error instead of a silent bad refund. The enum on reason makes invented categories impossible. This single schema converts a whole class of silent downstream failures into a loud, local one the model can fix on the next turn.

Common pitfalls

• Debugging from the final answer only. The bug lives in the middle of the trace. Always read the full turn-by-turn transcript, not just the output.
• Killing loops without explaining why. A bare stop wastes the partial progress. Inject a reflection message so the model can recover or report cleanly.
• Blaming the model for tool confusion. Check your tool descriptions and overlap first; the fix is usually in the metadata, not the weights.
• Trusting argument shape over argument truth. A schema-valid argument can still be hallucinated. Use patterns, enums, and an existence check (lookup before mutate) for anything that changes state.
• No deterministic replay. If you can't re-run a captured failure offline against recorded tool outputs, every debug cycle costs live tokens and the bug may not even reproduce.

Ship a debuggable agent in 6 steps

1. Instrument every tool call with turn index, inputs, raw outputs, latency, and token counts to a queryable store.
2. Add a turn budget and a repeat-call detector that injects a reflection message on the third identical call.
3. Rewrite tool descriptions as crisp "use this when / not when" docstrings and prune overlapping tools.
4. Define strict JSON Schema for every tool input, with patterns and enums, and validate before execution.
5. Return validation errors to the model as tool results so it can self-correct rather than crash.
6. Build a replay harness that re-runs captured transcripts against recorded tool outputs so you can fix and verify offline.

Loop vs. wrong-call vs. hallucinated-arg: how to tell them apart

Frequently asked questions

What is an agent loop, exactly?

An agent loop is a failure mode in which an autonomous agent repeatedly issues the same or near-identical tool calls without making measurable progress toward its goal, because nothing in its feedback signals that the current strategy has stopped working. It is broken by adding explicit progress checks: a turn budget, a repeat detector, and a forced strategy-change prompt.

How do I reproduce a flaky Claude agent run?

Capture the full structured transcript — system prompt, user input, every tool call, and every tool result — then replay it offline against the recorded tool outputs. Because the tool results are fixed, you isolate the model's decision-making from live API variance, which makes the failure far more reproducible and lets you verify a fix without paying for live calls each iteration.

Why does Claude sometimes call a tool with a made-up ID?

The model is pattern-completing a plausible-looking argument when it lacks the real value. The fix is structural, not promptual: constrain the argument with a JSON Schema pattern or enum, and require a lookup or existence check before any state-changing action so an invented ID fails validation immediately instead of executing.

Should I lower temperature to reduce tool-call errors?

Lower temperature reduces variance but does not fix a vague tool surface or a missing schema. Treat temperature as a minor knob. The durable fixes are clear tool descriptions, strict input validation, and progress guardrails, all of which work regardless of sampling settings.

From transcripts to live conversations

CallSphere takes the same discipline — strict tool schemas, loop guards, and replayable traces — and applies it to voice and chat agents that handle real customer calls and messages, invoke tools mid-conversation, and book work around the clock. See how it runs in production 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.