Debugging Claude Agents: Loops, Bad Tool Calls, Fixes (Claude Api Skill Ecosystem)

The first time an agent you built with the Claude Agent SDK goes off the rails, it rarely throws an exception. It just keeps going. It calls the same tool eleven times in a row with slightly different arguments, or it confidently passes a customer_id that never appeared anywhere in the conversation, or it answers a question it was supposed to escalate. Debugging agentic systems is not like debugging a function — there is no stack trace pointing at line 47. The bug lives in a transcript of model decisions, and you have to read it like a detective.

This post is a working taxonomy of the failure modes that show up when you ship Claude-powered agents, and the concrete handles you have for each. The good news is that almost every "the agent is broken" report collapses into one of a handful of patterns, and each pattern has a reproducible fix that doesn't require retraining anything.

Why agentic bugs are different

A traditional bug is deterministic: same input, same wrong output, every time. An agentic bug is a distribution of behaviors. Claude calls tools in a loop, and at each turn it samples a decision conditioned on everything that came before — the system prompt, the tool definitions, every prior tool result. A single poorly-worded tool description can shift the probability of a wrong call from 2% to 30%, and you'll only see it under load.

The single most useful debugging artifact is the full message array as it existed at the moment things went wrong. When you run a manual agentic loop, log response.content, response.stop_reason, and response.usage on every iteration. The stop_reason field alone resolves a surprising fraction of incidents: max_tokens means the response was truncated mid-thought (the agent looks "confused" because it never finished), tool_use means it wants another round trip, and refusal means it declined for safety reasons and your loop probably mishandled the empty result.

Failure mode one: the runaway loop

The classic. The agent calls a tool, gets a result it doesn't like, calls the same tool again, and never converges. In a tool-runner setup this manifests as a session that burns thousands of tokens and never returns; in a manual loop it's an iteration counter that climbs without bound.

flowchart TD
  A["Agent turn N"] --> B{"stop_reason?"}
  B -->|end_turn| C["Done — return answer"]
  B -->|tool_use| D["Execute tool"]
  D --> E{"Same tool + similar args\nas last 2 turns?"}
  E -->|No| F["Append result, loop"]
  E -->|Yes| G["Loop-guard tripped"]
  G --> H["Inject corrective message\n& cap iterations"]
  F --> A
  H --> A

The root cause is almost always an uninformative tool result. If your search_orders tool returns an empty array when nothing matches, Claude doesn't know whether the query was wrong or the data genuinely doesn't exist — so it retries with a tweaked query forever. Fix the tool, not the prompt: return {"matches": [], "reason": "no orders found for this account in the last 90 days; widen the date range or verify the account ID"}. A result that explains itself ends the loop because the next decision is now obvious.

Belt-and-suspenders: always cap the loop. In a manual loop, break after N iterations and surface a graceful failure. With the tool runner, you still want an outer guard — track recent (tool_name, hash(input)) pairs and, on a repeat, append a tool_result with is_error: true and a message like "You have already tried this exact call and it failed. Try a different approach or ask the user for clarification." Claude responds well to being told it's repeating itself.

Failure mode two: the wrong tool call

Here the agent reaches for issue_refund when it should have called check_refund_eligibility, or it uses a generic bash tool to do something you exposed a dedicated tool for. The cause is almost never the model's "intelligence" — it's the tool surface you handed it.

Two descriptions that overlap in meaning create a coin flip. If get_account and get_customer both say "retrieve customer information," Claude has no basis to choose. Rewrite descriptions to be prescriptive about when to call each: "Call get_account when you need billing status or plan tier. Call get_customer when you need contact details or support history." Recent Opus models reach for tools more conservatively and follow these trigger conditions closely, so the description is your highest-leverage lever.

When a single tool is dangerous, don't rely on the description alone. Set a permission gate so the harness intercepts the call before it executes — a manual loop where any issue_refund tool_use pauses for human approval. Reversibility is the criterion: hard-to-undo actions (refunds, deletions, outbound messages) deserve a confirmation step that a read-only glob does not.

Failure mode three: hallucinated arguments

The agent calls the right tool but invents a value. It passes order_id: "ORD-48217" when no such ID was ever mentioned. This is genuinely the most insidious failure because the call succeeds against a plausible-looking but wrong record.

The structural fix is strict tool schemas. Mark strict: true on the tool and constrain the input as tightly as the data allows — use enum for fixed value sets, format: "uuid" or a regex-shaped string for IDs, and mark only genuinely-required fields as required. Strict mode guarantees the JSON validates against your schema, which catches malformed arguments but not wrong-but-valid ones. For those, the tool itself must validate against reality and return an informative error: "No order ORD-48217 exists. Order IDs in this account: ORD-90011, ORD-90042." That turns a hallucination into a recoverable, self-correcting turn.

It also helps to give the model the data it needs before it needs it. If an agent keeps hallucinating account IDs, the IDs probably aren't in its context. Surface them in an earlier tool result rather than hoping the model remembers a value from six turns ago.

Reading the transcript like a debugger

When an agent misbehaves, replay the exact message array offline. Because the Messages API is stateless, you can reconstruct any failing state from logs and re-run it deterministically enough to bisect. Strip turns from the end until the bad decision disappears, then look at the last tool result before the agent went wrong — nine times out of ten the answer is right there: an empty result, an ambiguous error string, a truncated payload, or a tool description that competes with another.

A defining sentence worth keeping: an agentic failure mode is a recurring, model-level decision error — a loop, a wrong tool selection, or a fabricated argument — that arises from the tools and context you provided, not from a single line of broken code. Frame every incident that way and your fixes land on the tool surface and the prompt, where they belong.

Frequently asked questions

How do I stop a Claude agent from looping forever?

Combine two things: make every tool result self-explanatory (so the next decision is obvious), and add a hard loop guard that detects repeated (tool_name, input) pairs and injects a corrective tool_result with is_error: true. Cap total iterations as a final backstop.

Why does Claude call the wrong tool?

Usually overlapping tool descriptions. Make each description prescriptive about when to call it, not just what it does. If two tools could plausibly answer the same request, merge them or sharpen the boundary between them.

How do I catch hallucinated tool arguments?

Use strict: true schemas with enums and string formats to reject malformed input, and have the tool validate values against real data, returning an informative error that lists valid options when a value doesn't exist.

What's the fastest way to reproduce an agent bug?

Log the full message array, stop_reason, and usage on every loop iteration, then replay the exact array offline. The stateless Messages API lets you reconstruct and re-run the failing state to bisect the cause.

Bringing agentic AI to your phone lines

The same debugging discipline — informative tool results, tight schemas, loop guards — is what keeps CallSphere's voice and chat agents reliable on live calls, where a runaway loop or a hallucinated booking is a customer, not a log line. See how it works 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.