Debugging Claude Agents: Loops, Wrong Tools, Bad Args

When a Claude Managed Agent works, it feels like magic: you describe an outcome, and the agent plans, calls tools, and lands the result. When it breaks, the failure rarely looks like a stack trace. Instead the agent quietly burns tokens re-reading the same file, calls a tool with an argument it invented, or convinces itself a step succeeded when it never ran. Debugging an agent is less like reading logs and more like watching a junior engineer who narrates their reasoning out loud and occasionally lies to themselves.

This post is a field guide to the three failure modes that account for most broken agent runs — loops, wrong tool calls, and hallucinated arguments — and how to diagnose and fix each one inside a Claude Managed Agent. The mindset shift that matters most: an agent failure is almost always a context problem or a feedback problem, not a model problem.

Key takeaways

• The three dominant agent failures are loops, wrong tool selection, and hallucinated arguments — each has a distinct signature in the transcript.
• Loops almost always trace back to a tool that returns the same unhelpful result or an error the agent cannot interpret; fix the tool's output, not the prompt.
• Wrong tool calls come from overlapping tool descriptions and missing "when NOT to use this" guidance.
• Hallucinated arguments are a schema problem: tight JSON schemas with enums and required fields cut them dramatically.
• The single most valuable debugging artifact is a full, replayable transcript with every tool input and raw output.

Why agent debugging is different

A traditional program fails deterministically: the same input produces the same crash. A Claude Managed Agent is a loop where the model reads the accumulated context, decides on the next action, executes a tool, appends the result, and repeats until it believes the outcome is met. Every step depends on a stochastic decision made over a growing context window. That means a bug can appear on run 7 and vanish on run 8 with identical inputs.

The practical consequence is that you cannot debug by re-running and hoping. You debug by capturing the full transcript — system prompt, every assistant message, every tool call with its exact arguments, and every raw tool result — and reading it like a detective. The question is never "is the model dumb?" It is "what did the agent see at the moment it made the bad decision, and what would a competent engineer have done with that same context?" Nine times out of ten the agent saw something genuinely confusing, and the fix is to make the context clearer.

The three failure modes and their signatures

Most broken runs fall into one of three buckets. Learning their signatures lets you classify a failure in seconds instead of staring at hundreds of lines of transcript.

flowchart TD
  A["Agent run misbehaves"] --> B{"Same action repeated 3+ times?"}
  B -->|Yes| C["Loop: tool returns unhelpful or unparsed result"]
  B -->|No| D{"Right action, wrong tool chosen?"}
  D -->|Yes| E["Tool confusion: overlapping descriptions"]
  D -->|No| F{"Tool args invented or malformed?"}
  F -->|Yes| G["Hallucinated args: loose schema"]
  F -->|No| H["Outcome misjudged: weak success check"]
  C --> I["Fix tool output + add retry guidance"]
  E --> I
  G --> I
  H --> I

Loops are the most common and the most expensive. The agent calls a search tool, gets an empty result, calls it again with a near-identical query, gets the same empty result, and repeats. The signature is obvious in a transcript: the same tool name appears back to back with tiny variations. The root cause is almost never the model being stubborn — it is a tool that returns an output the agent cannot act on. An empty array with no explanation reads, to the model, as "maybe I phrased it wrong, let me try again."

Wrong tool calls show up when the agent picks create_ticket when it should have called update_ticket, or uses a generic search when a specialized lookup_customer exists. The signature is a plausible-but-wrong action that the agent commits to confidently. This is a tool-design failure: two tools whose descriptions overlap, or a powerful general tool that out-competes the specific one.

Hallucinated arguments are the scariest because they can succeed silently. The agent calls a tool with {"region": "us-west-7"} for a region that does not exist, or passes a customer ID it pattern-matched from earlier text. The signature is an argument value that never appeared verbatim in the context. Tight schemas are the cure.

Fixing loops: make tool output actionable

The instinct is to add "do not repeat yourself" to the system prompt. That rarely works because the agent does not believe it is repeating itself — it believes each attempt is a refinement. The durable fix is on the tool side. Every tool result should answer two questions for the model: did this succeed, and if not, what specifically should change?

Compare a bad result with a good one. A search tool that returns [] teaches the agent nothing. A search tool that returns the structure below tells the agent exactly why it failed and what to do next, which breaks the loop.

{
  "status": "no_results",
  "query_received": "premium tier customers in EU",
  "reason": "filter 'tier=premium' matched 0 rows; valid tiers are: basic, pro, enterprise",
  "suggestion": "retry with tier in [basic, pro, enterprise] or remove the tier filter",
  "retryable": true
}

Notice the retryable flag and the explicit list of valid values. The agent now knows whether trying again could possibly help and what a valid retry looks like. As a backstop, the orchestrator should enforce a hard loop limit — if the same tool is called more than a small number of times with semantically similar arguments, halt and surface the partial state rather than letting the run spend tokens indefinitely.

Fixing wrong tool calls: disambiguate at the description layer

Claude chooses tools primarily from their names and descriptions, so ambiguity there is the real bug. The fix is to write descriptions that include negative guidance — an explicit statement of when not to use a tool. A description like "Search the knowledge base. Do NOT use this to look up a specific customer by ID; use lookup_customer for that" resolves the most common confusion outright.

When two tools genuinely overlap, the better move is to merge them into one tool with a mode parameter, or to remove the weaker one. Fewer, sharper tools beat a sprawling catalog. If your agent has twenty tools and routinely picks wrong, the problem is the catalog, not the model. Audit which tools are actually used across real runs and prune the ones that only cause confusion.

Fixing hallucinated arguments: constrain the schema

The most effective single change you can make is tightening your tool input schemas. Replace free-form strings with enums wherever the set of valid values is known. Mark every truly required field as required so the model cannot omit it and improvise. Add format constraints — patterns for IDs, ranges for numbers — so an invented value fails validation at the boundary instead of reaching your backend.

Crucially, validation errors must be returned to the agent in a form it can use, not swallowed. When the agent passes an invalid enum value, the tool should reply with the allowed values so the next attempt is correct. The combination of a strict schema plus an informative validation error converts a silent hallucination into a self-correcting step. This is the same discipline you would apply to a public API; an agent is just a very fast, very literal API client.

Common pitfalls

• Debugging from summaries instead of raw transcripts. A summarized log hides the exact argument that was hallucinated. Always capture and read the full, unredacted tool input and output.
• Patching the system prompt for tool-layer bugs. If a tool returns useless errors, no prompt wording will reliably fix the loop. Fix the tool's output contract first.
• Letting tool counts grow unbounded. Every tool you add makes selection harder. Treat the tool catalog as a product surface and keep it minimal.
• Trusting the agent's self-reported success. Agents will declare victory without verifying. Add an explicit verification step that checks the real-world state, not the agent's belief.
• Testing only the happy path. Most failures live in the error branches. Deliberately feed the agent empty results, timeouts, and malformed data to see how it recovers.

Debug a broken agent run in 6 steps

1. Capture the full transcript: system prompt, every assistant turn, every tool call with exact arguments, every raw tool result.
2. Classify the failure as a loop, a wrong-tool selection, or a hallucinated argument using the signatures above.
3. Find the first bad decision — the earliest point where the agent went wrong — and read the context it had at that moment.
4. Ask whether a competent human would have made the same mistake given only that context. If yes, the context is the bug.
5. Apply the targeted fix: actionable tool output for loops, disambiguated descriptions for wrong tools, strict schemas for hallucinated args.
6. Re-run the same scenario several times, not once, since agent behavior is stochastic; only call it fixed when it is consistently correct.

Symptom	Likely root cause	Primary fix
Same tool called repeatedly	Unactionable tool output	Structured results with reason + retryable flag
Plausible but wrong tool chosen	Overlapping descriptions	Negative guidance; merge or prune tools
Invented argument values	Loose input schema	Enums, required fields, format constraints
False claim of success	No verification step	Check real state before completing

Frequently asked questions

How do I tell a loop from legitimate iteration?

Look at whether the arguments meaningfully change and whether new information arrives. Legitimate iteration narrows toward an answer — each call uses what the last one returned. A loop repeats near-identical calls that receive the same uninformative result. If three consecutive calls produce no new context, it is a loop.

Should I lower the temperature to reduce hallucinated arguments?

It helps marginally but it is not the real fix. Hallucinated arguments are a schema and grounding problem. A strict schema with enums and a validation error that lists valid values does far more than any temperature change, and it keeps working even when the agent is being creative for good reasons elsewhere.

What is the single most useful thing to log?

The exact tool inputs and raw tool outputs for every step, in order. Most teams log the agent's natural-language reasoning but truncate the tool I/O, which is exactly the data you need to diagnose loops and hallucinations. Log the structured I/O first; reasoning text is secondary.

Can I unit-test an agent the way I test a function?

Not with single assertions, because outputs vary run to run. You test agents by replaying recorded scenarios and asserting on outcomes and behaviors — did it call the right tool, did it reach the goal — across multiple runs, accepting a pass rate rather than a single boolean.

Bringing agentic AI to your phone lines

CallSphere puts these same debugging disciplines — actionable tool outputs, strict schemas, and verified outcomes — behind voice and chat agents that answer every call, use tools mid-conversation, and book real work around the clock. See it live 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.