Debugging Claude Cowork: Loops, Bad Tool Calls, Fixes

The first week I pointed Claude Cowork at a real sales book — four thousand accounts spread across a CRM, an enrichment connector, and a calendar — it did roughly eighty percent of the work flawlessly and then spent twenty minutes looping on the same account because a phone-number field came back as null. That single bug taught me more about agentic debugging than any tutorial. When an agent runs hundreds of tool calls across thousands of records, the question is never "did it work?" It's "where, exactly, did the reasoning go sideways, and why didn't it notice?"

This post is a field guide to the failure modes you actually hit at book-management scale in Claude Cowork, and the concrete instrumentation that turns a frustrating black box into something you can debug like ordinary software.

Why agentic bugs feel different

A traditional script fails loudly: a stack trace, a nonzero exit code, a line number. An agent fails quietly and creatively. It will route around a missing field, invent a plausible value, or decide that re-reading the same record is progress. The model is optimizing to complete your goal, and that optimization pressure is exactly what produces the weird behavior. A debugging failure mode is any deviation between the agent's internal model of the task and the real state of your tools and data — and the dangerous ones are the deviations the agent hides from itself.

At four thousand accounts, three categories cause almost all the pain: loops, where the agent repeats an action without making progress; wrong tool calls, where it picks a valid-but-incorrect tool or the right tool with the wrong intent; and hallucinated arguments, where it fabricates an ID, an email, or a parameter that was never in the data. Each has a different smell and a different fix.

Loops: when re-trying becomes the whole job

Loops are the most common and the most expensive. The classic shape is a retry loop: a tool call returns an error or an empty result, the agent re-reads the situation, decides to call the same tool again, gets the same result, and repeats. Because each iteration looks like reasonable reasoning in isolation, nothing trips an alarm. I've watched Cowork burn fifteen iterations trying to update a record that was locked by another process.

The fix starts with making loops observable. Log every tool call as a structured event — tool name, a hash of the arguments, the result status — and run a simple detector: if the same (tool, argument-hash) pair fires more than N times in a window, that's a loop. The detector matters more than any clever prompt, because the agent genuinely cannot tell that it's stuck.

flowchart TD
  A["Agent picks next action"] --> B["Tool call executes"]
  B --> C{"Same tool+args seen >3x?"}
  C -->|No| D["Record event, continue"]
  C -->|Yes| E["Loop detector fires"]
  E --> F{"Result still failing?"}
  F -->|Yes| G["Halt, flag account for human"]
  F -->|No| H["Break loop, move to next account"]
  D --> A

Beyond detection, give the agent an explicit escape hatch in its instructions: "If a tool returns the same error twice for one account, stop retrying, write the account ID and the error to the needs_review list, and move on." Naming the exact behavior you want — skip and log, not retry harder — is what converts a silent twenty-minute loop into a one-line entry in a queue a human can clear in seconds.

Wrong tool calls: right hammer, wrong nail

The second failure mode is subtler. The agent calls a real, working tool — it just calls the wrong one, or the right one for the wrong reason. In a sales book this looks like updating the last_contacted field when it meant to update last_attempted, or sending an enrichment query to the company-lookup tool when it needed the person-lookup tool. Nothing errors. The data is simply, quietly wrong.

Two things drive wrong tool calls: overlapping tool descriptions and missing preconditions. If two MCP tools have descriptions that sound similar, the model has to guess, and at scale it will guess wrong some fraction of the time. Tighten the descriptions so each tool's purpose is unambiguous, and state when not to use it ("Use update_contact only for fields the rep owns; never touch account_status, which billing owns"). The second lever is read-before-write: instruct the agent to fetch and confirm a record's current state before mutating it, and to include the record ID it's about to change in its reasoning so you can audit the chain.

The best catch for wrong tool calls is a dry-run mode. Run the whole book with every write tool stubbed to log its arguments instead of executing, then diff the proposed mutations against your expectations before letting it touch production. You find the wrong-tool patterns on a sample of fifty accounts instead of discovering them across four thousand.

Hallucinated arguments: the fabricated ID problem

Hallucinated arguments are the failure mode that scares people, and rightly so. The agent needs an account ID it doesn't have, so it produces one that looks right — same format, plausible prefix — and passes it to a tool. Sometimes the tool errors. Worse, sometimes that fabricated ID happens to match a real, different record, and you've just written a note to the wrong customer.

The defense is to never let the model originate identifiers. Identifiers should flow from tool outputs into tool inputs as opaque tokens the agent passes through but never invents. Enforce this with validation at the tool boundary: before any write, check that the ID being used appeared in a prior read result for this run. If it didn't, reject the call and surface it. This is cheap to implement and it converts a class of silent data-corruption bugs into loud, catchable errors.

Prompting helps too. Tell the agent explicitly: "You may only use account IDs, contact IDs, and emails that you have read from a tool in this session. If you need an identifier you don't have, search for it — never construct it." Pairing that instruction with boundary validation gives you defense in depth: the prompt reduces frequency, the validator catches what slips through.

Building a debugging loop you can trust

The teams who run large books on Cowork successfully treat the agent like a distributed system, not a chatbot. They keep a run transcript — every prompt, tool call, argument, and result — and they tag each tool call with the account it touched so failures can be traced to specific records. When something goes wrong on account 2,317, you can replay exactly what the agent saw and did, the same way you'd read a request trace in a microservice.

Start small and widen the blast radius deliberately: ten accounts, then a hundred, then the full book. At each step you're not just checking output quality, you're checking the failure rate — how many accounts landed in needs_review, how many loops fired, how many writes the validator rejected. Those counters are your test suite. When they hold steady as you scale from a hundred to a thousand accounts, you've actually debugged the system rather than gotten lucky on a small sample.

Frequently asked questions

How do I stop Claude Cowork from looping on one record?

Add a loop detector that hashes each tool call's name and arguments and halts after the same pair repeats a few times, and give the agent an explicit instruction to log the account to a review queue and move on after two identical failures. Don't rely on the agent noticing on its own — it usually can't.

What causes hallucinated tool arguments?

They happen when the agent needs a value, like an account ID, that isn't in its current context, so it generates a format-plausible substitute. Prevent it by validating at the tool boundary that every identifier was read from a prior tool result in the same run, and by instructing the agent to search for IDs rather than construct them.

How can I test agent behavior before running the whole book?

Use a dry-run mode where write tools log their proposed arguments instead of executing. Run a sample of accounts, diff the proposed mutations against what you expect, and only point it at production once the diff is clean. This surfaces wrong-tool and hallucination patterns on fifty records instead of four thousand.

Should I debug by reading the model's reasoning or its tool calls?

Tool calls and their results are the ground truth — the reasoning text explains intent, but the calls are what actually changed your data. Log both, but trust the structured call log when they disagree. The most dangerous bugs are exactly the ones where confident reasoning accompanies a wrong or fabricated call.

Bringing agentic AI to your phone lines

The same loop detection, tool-boundary validation, and replayable transcripts that keep a Cowork sales book honest are what make a voice agent trustworthy. CallSphere brings these agentic-AI patterns to voice and chat — assistants that answer every call, use tools mid-conversation, and book work around the clock. 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.