Debugging the Message Batches API: Loops & Bad Tool Calls

The Message Batches API is deceptively easy to start with and surprisingly hard to debug. You submit a few thousand requests, walk away, and come back to a results file where 4% of your jobs came back wrong, another 1% errored out, and a stubborn handful never finished at all. Because each request runs detached from your code, you cannot drop a breakpoint into the middle of a run the way you would with a synchronous call. By the time you see a failure, the model has already made its decision and moved on. This post is about the specific failure modes that show up when you process agentic work at scale with Claude's batch endpoint, and the concrete tactics that turn an opaque results file into something you can actually reason about.

Key takeaways

• Batch failures fall into a few repeatable buckets: tool-call loops, wrong tool selection, hallucinated arguments, schema-invalid output, and silent truncation — each has a different fix.
• Always set a custom_id that encodes your own row key, so you can join results back to inputs without guessing.
• Use stop_reason and the structured error object as your first triage signal before you ever read the model's text.
• Reproduce a single failing batch item synchronously to debug it — the same model and prompt behave identically.
• Cap agentic loops with a hard turn limit and validate tool arguments against a JSON Schema before you execute them.
• Keep a small "golden" replay set so a prompt change that fixes one failure does not silently break ten others.

Why batch debugging is different

The Message Batches API lets you submit large collections of message requests as a single asynchronous job, processed within roughly a day at a discounted rate compared to standard synchronous calls. That asynchronicity is the whole point — and also the whole problem. With a normal request you see the model reason, call a tool, and respond in one tight feedback loop you can step through. With a batch, thousands of independent conversations run out of sight, and you only inspect the wreckage afterward.

This changes how you debug. You cannot interactively probe a stuck run, so you have to make every request self-describing before you submit it. The two levers that matter most are the custom_id you attach to each request and the structured metadata Claude returns: stop_reason, token counts, and the per-request error object. If you treat those as your debugger, most failures become quickly diagnosable. If you ignore them and only read the prose Claude produced, you will spend hours pattern-matching by eye.

The mental shift is from "watch it run" to "make it explain itself." Every input carries an ID you control; every output carries a status you can branch on. Debugging at scale is mostly the discipline of preserving that join key and reading the metadata first.

The five failure modes you will actually hit

Tool-call loops. The agent calls a tool, gets a result it does not know how to use, and calls the same tool again — sometimes forever, until it hits the token ceiling. In a batch you see this as a request that consumed an enormous number of output tokens and ended with stop_reason of max_tokens. The fix is a hard cap on agent turns inside your harness, plus a rule that the same tool with the same arguments is never called twice in a row.

Wrong tool selection. Claude picks search_orders when it should have picked search_customers. This usually traces to overlapping tool descriptions. When two tools sound similar, the model guesses. Tighten the descriptions so each one names exactly when to use it and when not to.

Hallucinated arguments. The model invents an order_id that was never in the conversation, or passes a date in the wrong format. This is the single most common cause of "the tool ran but returned nothing." Defend against it by validating every argument against the tool's input_schema before execution and returning a corrective tool-result when validation fails.

Schema-invalid final output. You asked for JSON and got JSON wrapped in an apology, or with a trailing comment. Parse defensively and, on failure, send the parse error back as a follow-up turn.

Silent truncation. The response hit max_tokens mid-object. The JSON looks fine until the last few characters. Always check stop_reason before you trust the body.

flowchart TD
  A["Batch result row"] --> B{"stop_reason?"}
  B -->|max_tokens| C["Truncated or looping"]
  B -->|tool_use| D["Validate tool args vs schema"]
  B -->|end_turn| E["Parse final output"]
  C --> F["Cap turns & raise limit"]
  D -->|invalid| G["Hallucinated args -> corrective turn"]
  D -->|valid| H["Execute tool"]
  E -->|parse fails| I["Send parse error back"]
  E -->|ok| J["Accept & store"]

Make every request self-describing

The cheapest debugging investment you can make is a disciplined custom_id. Encode the primary key of your source row, plus a prompt version, so that when a result comes back wrong you know exactly which input produced it and which prompt was in play. Here is the shape of a batch request that does this:

{
  "custom_id": "ticket-48213|promptv7",
  "params": {
    "model": "claude-sonnet-4-6",
    "max_tokens": 1024,
    "tools": [ /* your tool definitions */ ],
    "messages": [
      { "role": "user", "content": "Classify and route this ticket: ..." }
    ]
  }
}

When you stream the results back, the first thing you read is not the content — it is the envelope. A result is either succeeded, errored, canceled, or expired. Branch on that, then on stop_reason, and only then look at the actual message. A triage loop that checks the envelope first turns "why did 50 rows fail" from an afternoon into ten minutes.

Reproduce one failure synchronously

Once you have isolated a failing custom_id, do not debug it inside the batch. Pull that exact request, fire it as an ordinary synchronous message with the same model and parameters, and watch it run. The batch endpoint is not a different model — it is the same model on a different delivery channel — so the behavior reproduces. Now you can inspect the full tool-use trace, tweak the prompt or tool descriptions, and confirm the fix interactively before you re-batch the corrected rows.

This single-item replay habit is the backbone of scaled debugging. Batches are for volume; synchronous calls are for understanding. Keep a tiny script that takes a custom_id, looks up the original params from your input file, and replays them. You will use it constantly.

Common pitfalls

• Reading text before metadata. You assume a row "looks fine" but it was truncated. Always gate on stop_reason first; a body that ends mid-JSON with max_tokens is a failure no matter how plausible it reads.
• Reusing index position as the key. Batch results do not guarantee input ordering. If you join by array index instead of custom_id, you will silently attribute outputs to the wrong inputs.
• No turn cap on agentic loops. Without a maximum-turns guard, a single confused conversation can burn your entire output budget. Cap turns and treat hitting the cap as a failure to investigate, not a success.
• Trusting tool arguments unchecked. Executing a tool with hallucinated arguments produces garbage that the model then reasons over. Validate against input_schema and reject early.
• Fixing one row, breaking ten. A prompt edit that resolves a single failure can regress others. Replay a golden set after every change.

A debugging checklist you can run today

1. Add a meaningful custom_id to every request that encodes your row key and prompt version.
2. When results arrive, bucket each row by result type, then by stop_reason, before reading any content.
3. For every tool_use turn, validate arguments against the tool's input_schema and log validation failures separately.
4. Pull each distinct failure mode's first example and replay it synchronously to find the root cause.
5. Patch the prompt or tool descriptions, replay your golden set, and confirm no regressions.
6. Re-batch only the failed custom_ids, not the whole job, to save tokens and time.

Symptom	Likely cause	First fix
Huge output, ends mid-text	Tool-call loop	Hard turn cap + no-repeat rule
Tool ran, returned empty	Hallucinated arguments	Schema-validate before execute
Wrong tool fired	Overlapping descriptions	Disambiguate tool docs
JSON parse error	Truncation or wrapper text	Check stop_reason, send parse error back

Frequently asked questions

How do I tell a loop apart from a legitimately long task?

Look at the tool-use trace. A loop repeats the same tool with near-identical arguments and makes no progress toward the goal; a long task calls different tools or the same tool with advancing arguments. If you log each tool call's name and a hash of its arguments, a loop shows up as a repeated hash. A hard turn cap catches both, but only the trace tells you which one you had.

Can I cancel a batch once I see it going wrong?

Yes. The Message Batches API supports canceling an in-progress batch, after which requests that were not yet processed end in a canceled state while already-finished ones remain available. This is useful when you spot a systemic prompt bug early — cancel, fix, and resubmit rather than paying for thousands of broken runs.

Why did some requests come back as expired?

A batch has a processing window of roughly 24 hours; any request not completed in that window is returned with an expired status. Expirations usually mean individual requests were extremely long or the batch was very large. Shrink per-request max_tokens, split oversized batches, and cap agent turns so no single conversation monopolizes the window.

Should I retry failed rows automatically?

Retry transient errors (rate or server errors) with backoff, but never blind-retry logical failures like hallucinated arguments — the same prompt will fail the same way. Route logical failures to your replay-and-fix loop instead, and only re-batch them after you have changed the prompt or tools.

Bringing agentic AI to your phone lines

CallSphere takes these same debugging disciplines — self-describing requests, metadata-first triage, and tight tool validation — and applies them to voice and chat agents that handle every call and message, use tools live in the conversation, and book work around the clock. See it in action at callsphere.ai.

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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.