Debugging Claude Cowork agents: loops and bad tool calls

The first time an agent runs perfectly in a demo and then quietly burns through forty turns on a real task, you learn something the marketing never tells you: agentic systems fail in ways traditional software does not. There is no stack trace pointing at a null pointer. Instead you get a Claude Cowork run that called the same connector eleven times, passed a date that does not exist, or politely announced it had finished a task it never started. This post is a practical taxonomy of those failures and a repair manual for each one.

Claude Cowork is Anthropic's agentic product for non-engineering knowledge work, where plugins bundle Skills, MCP connectors, and sub-agents so Claude can plan and act across your tools. Because the model decides what to do at each step rather than following a fixed script, debugging shifts from reading code to reading behavior. You are debugging a reasoning process, and the trace is your most important instrument.

Why agent failures look nothing like normal bugs

A deterministic program does the same wrong thing every time, which makes it reproducible. An agent is stochastic and context-dependent: the same prompt against the same connectors can succeed at 9am and loop forever at 9:05 because a tool returned a slightly different payload. That non-determinism is the core difficulty. The bug is rarely in one line; it lives in the interaction between the instructions, the tool descriptions, and whatever the environment handed back.

The practical consequence is that you cannot debug agents by staring at the final output alone. You need the full transcript: every tool call, every argument, every observation the model received, and the reasoning between them. Treat the run trace the way a backend engineer treats logs. If your platform does not surface the intermediate tool calls and their raw results, fixing anything is guesswork.

Failure mode one: the infinite or near-infinite loop

Loops are the most common and most expensive failure. The agent calls a tool, gets a result it does not know how to act on, and tries again — sometimes with identical arguments, sometimes with tiny meaningless variations. A classic example: a Cowork agent asked to "find the latest invoice and summarize it" keeps re-querying a document connector because the connector returns a list and the agent expects a single record, so it never feels it has the answer.

flowchart TD
  A["Agent receives task"] --> B["Calls a connector"]
  B --> C{"Result usable?"}
  C -->|Yes| D["Advance to next step"]
  C -->|No, ambiguous| E["Re-call same tool"]
  E --> F{"Loop guard tripped?"}
  F -->|No| B
  F -->|Yes| G["Stop & ask user / summarize state"]
  D --> H["Task complete"]

The cure is structural, not a better prompt. First, give the agent an explicit stopping condition and a turn budget so a loop guard trips before costs spiral. Second, fix the tool contract: if a connector returns a list when the agent wants one item, either narrow the query upstream or add a description telling the model how to pick. Third, make tool results legible — a connector that returns a raw 4,000-token JSON blob invites loops because the model cannot reliably extract the field it needs.

A subtler loop is the oscillation: the agent toggles between two strategies, undoing its own work. This usually signals conflicting instructions — one Skill says "always confirm before writing" while the task says "just do it." Resolve the contradiction at the source rather than hoping the model picks the right horn.

Failure mode two: the wrong tool for the job

When an agent has a dozen connectors, choosing the right one becomes a retrieval problem the model solves from the tool descriptions. Vague or overlapping descriptions are the root cause of wrong tool calls. If two connectors both say "search company data," the agent will pick one at random and you will see it query the CRM when it should have queried the data warehouse.

The fix is to write tool descriptions like API documentation for a junior colleague: state exactly what the tool does, what it does not do, what inputs it expects, and when to prefer it over a sibling. Disambiguation is your job, not the model's. A description that ends with "Use this for X; for Y use the other connector instead" dramatically cuts misrouting. Reducing the number of available tools per task also helps — a focused plugin with three relevant connectors outperforms a kitchen-sink setup with thirty.

Failure mode three: hallucinated arguments

The most insidious failure is the confident, well-formed tool call with fabricated inputs: an invented record ID, a customer name that does not exist, or a date the agent reasoned its way into rather than reading from real data. The call looks valid, so it sails past naive validation and corrupts downstream steps.

Defend against this in two layers. At the schema layer, make tool inputs strict — enums, required fields, and format constraints so a malformed argument is rejected before it executes. At the grounding layer, require that identifiers come from a prior tool result, not from the model's memory. A reliable pattern is a two-step flow: first call a lookup tool that returns canonical IDs, then call the action tool using only those IDs. If the action tool receives an ID that never appeared in a prior observation, that is a hallucination you can catch and halt on.

Reading the trace like a profiler

When a run misbehaves, walk the transcript in order and ask three questions at each tool call: did the model have the information it needed to choose this tool, were the arguments grounded in real observations, and did the result actually help it move forward. The first place all three answers turn to "no" is your bug. Most of the time the root cause sits one or two steps earlier than the visible symptom — a confusing tool result that quietly derailed everything after it.

Keep a small library of failing transcripts. They become regression tests: when you change a tool description or tighten a schema, replay the old failures and confirm they now succeed. Over time this transcript library is worth more than any amount of prompt tweaking, because it captures the real, weird ways your specific environment breaks.

Frequently asked questions

How do I stop a Claude Cowork agent from looping?

Set an explicit turn budget and a clear stopping condition, then fix the underlying cause: usually a tool that returns ambiguous or oversized results the model cannot act on. A loop guard limits damage, but legible tool outputs prevent the loop entirely.

Why does my agent call the wrong connector?

Almost always because two tool descriptions overlap. Rewrite them to state precisely what each tool does and does not do, add explicit "use this instead" guidance, and reduce the number of tools exposed for a given task.

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

A hallucinated argument is a tool input the model fabricated rather than read from real data, such as a made-up record ID. Catch it by enforcing strict input schemas and requiring that identifiers originate from a prior tool result before any action tool runs.

Do I need a special debugger for agents?

You need full visibility into the run: every tool call, its arguments, and the raw result the model saw. With that transcript you can debug by reasoning about behavior; without it you are guessing. Save failing transcripts as regression tests.

Bringing agentic AI to your phone lines

The same debugging discipline — legible tool results, grounded arguments, and loop guards — is what makes CallSphere's voice and chat agents reliable enough to answer every call and book real work, not just demo well. See it live at callsphere.ai.