Debugging Claude Managed Agents: Loops, Bad Tool Calls

The first agent you ship will look great in the demo and then do something baffling in production: it calls the same tool eleven times in a row, passes a customer ID where a date should go, or confidently invents an argument that no tool ever accepted. Debugging a Claude Managed Agent is different from debugging ordinary software because the control flow lives partly in a language model's head. You can't set a breakpoint inside Claude's reasoning. What you can do is make the run observable, recognize the handful of failure modes that account for most incidents, and fix them at the layer where they actually originate.

This post is a practical guide to the three failures that dominate real bug reports — loops, wrong tool calls, and hallucinated arguments — plus the tracing setup that lets you catch them before a customer does.

Why agent bugs don't look like normal bugs

A traditional stack trace points at one line of code. An agent failure points at a decision: at some step, Claude looked at the conversation so far, the available tools, and the latest tool result, and chose a next action that was wrong. The bug is rarely in your code and rarely in the model weights — it lives in the gap between them, in tool descriptions that are ambiguous, results that are formatted confusingly, or a system prompt that quietly contradicts itself.

That means the unit of debugging is the turn: one model invocation, its full input context, the tool call it emitted, and the result that came back. If you can replay a single turn in isolation and watch what Claude does with slightly different inputs, you can find the root cause in minutes. If all you have is "the agent did something weird," you'll be guessing for hours.

An agent trace is the ordered record of every turn in a run — the messages, tool calls, tool results, and token counts — captured in enough detail to replay or inspect any single step. Building that trace is the prerequisite for everything else in this post.

Failure mode one: the runaway loop

Loops are the most common and most expensive failure. Claude calls a tool, gets a result it doesn't know how to use, and tries again — sometimes with identical arguments, sometimes cycling between two tools forever. The classic shape is a search-then-refine loop where every search returns nothing useful, so the agent keeps reformulating the query without ever concluding the answer isn't there.

flowchart TD
  A["Claude plans next step"] --> B{"Repeat of recent call?"}
  B -->|No| C["Execute tool call"]
  B -->|Yes, 2nd time| D["Inject hint: result unchanged, try a different approach"]
  B -->|Yes, 3rd time| E["Halt turn, surface to fallback"]
  C --> F["Return result to Claude"]
  D --> F
  F --> G{"Task complete?"}
  G -->|No| A
  G -->|Yes| H["Emit final answer"]

The fix is rarely "make Claude smarter." It's to give the loop a floor and a hint. Track a hash of recent tool calls; when you see the same call twice, inject a short system note like "that call returned the same result — a different tool or an honest 'not found' may be correct." On the third identical call, halt the turn and hand off to a fallback rather than burning more tokens. A hard step cap (say, 25 tool calls per run) is your last line of defense, but if you hit it often, the cap is hiding a design problem upstream.

Many loops trace back to a tool that returns an empty array on failure with no explanation. Claude can't tell "no results" from "you queried wrong," so it keeps trying. Returning a structured result with a status and a human-readable reason kills more loops than any prompt change.

Failure mode two: wrong tool calls

Here Claude picks a real tool but the wrong one for the job: it calls refund_order when the user asked to check a refund, or reaches for a generic search when a precise lookup tool exists. This is almost always a tool-description problem. When two tools have overlapping descriptions, the model has to guess, and under ambiguity it guesses based on superficial name similarity.

Audit your tool set the way you'd audit an API. Each tool's description should state what it does, when to use it, and — critically — when not to use it. Add a one-line disambiguator: "Use get_order_status for read-only checks; use modify_order only when the user explicitly asks to change something." Where two tools are genuinely close, consider merging them behind one tool with a mode argument, which removes the choice entirely.

The debugging move is to replay the offending turn with the tool descriptions tightened and see whether the right call now appears. Because you can hold the rest of the context fixed and vary only the descriptions, you get a clean causal read in one or two iterations.

Failure mode three: hallucinated arguments

The third pattern is subtler: Claude calls the right tool but fabricates an argument value — a plausible-looking order ID it never actually retrieved, a field name your schema doesn't have, or a date in the wrong format. The model is pattern-matching to what an argument should look like rather than to a value it genuinely knows.

Three defenses compound well. First, strict input schemas with tool use: define every argument with a JSON schema, mark required fields, and constrain enums and formats so an invalid value is rejected before it executes. Second, return an actionable error — "order_id must be a 10-digit string; received 'the customer's order'" — so the next turn can self-correct instead of guessing again. Third, when an argument must come from earlier in the conversation, make the source explicit: tools that depend on a prior lookup should fail loudly if that lookup hasn't happened, rather than accepting whatever the model supplies.

Validation at the boundary is non-negotiable. Treat every tool call as untrusted input, exactly as you would an HTTP request from a client you don't control.

The tracing setup that makes all of this fast

None of the above works without observability. At minimum, log for every turn: the full message list sent to Claude, the tool calls returned, the raw tool results, token counts split by input and output, and wall-clock latency. Tag each run with a stable ID so you can pull the whole sequence. With the Claude Agent SDK you can hook each tool call and persist these records as you go.

The highest-leverage capability is deterministic replay: take a captured run, freeze the recorded tool results, and re-execute the model decisions so you can change one variable — a tool description, a system prompt line, a result format — and watch the behavior shift. This turns "reproduce the bug" from a coin flip into a procedure. Pair replay with a small library of known-bad runs and you have the beginnings of a regression suite that catches loops and wrong calls before release.

Pitfalls that masquerade as model failures

Before you blame Claude, rule out the environment. A tool that intermittently times out will look exactly like an agent that "randomly" loops, because the model keeps retrying a flaky call. Results that exceed the context window get truncated, so the agent appears to "forget" something it was just told. And a system prompt that grows by accretion — every incident adds another rule — eventually contradicts itself and produces erratic behavior that no single change explains. Keep the prompt lean and version it; treat every added instruction as a potential new bug.

Frequently asked questions

How do I stop a Claude agent from looping forever?

Combine three guards: detect repeated tool calls by hashing arguments and inject a corrective hint on the second repeat, halt to a fallback on the third, and enforce a hard step cap per run. Most loops also dissolve once tools return a clear status and reason instead of an empty result.

What causes hallucinated tool arguments?

Claude fills an argument by pattern-matching when it lacks a real value. Defend with strict JSON schemas on every argument, actionable validation errors that let the next turn self-correct, and tool designs that require values to come from a prior verified step rather than from the model's imagination.

How do I reproduce an agent bug reliably?

Capture full per-turn traces with a stable run ID, then replay the run with recorded tool results frozen so the only thing changing is the model's decisions. Vary one input at a time — a tool description, a prompt line — to isolate the cause deterministically.

Is the bug usually in the model or my code?

Almost always in the seam between them: ambiguous tool descriptions, confusing result formats, or a contradictory system prompt. Fix those layers before reaching for a different model.

From debuggable agents to dependable phone lines

CallSphere takes these same debugging disciplines — per-turn tracing, loop guards, and strict tool validation — and applies them to voice and chat agents that handle real calls and messages, use tools mid-conversation, and book work around the clock. See how it runs in production 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.