Debugging Claude Agents: Loops, Bad Tool Calls, Bad Args

The day a security team flips on an autonomous Claude agent to triage alerts, the first thing they discover is that a misbehaving agent does not crash politely. It spins. It calls the same enrichment tool eleven times against the same indicator, burns a thousand dollars of tokens overnight, and produces a confident summary built on an argument it invented. When attackers are using AI to move faster, you cannot afford a defensive agent that quietly wanders off the rails. Debugging agentic systems is its own discipline, and the failure modes are surprisingly consistent across teams.

This post walks through the three failures you will hit most often when building with the Claude Agent SDK and Claude Code — runaway loops, wrong tool calls, and hallucinated arguments — and gives you a concrete, reproducible way to catch and fix each. The examples lean on a security-operations use case, because that is where the cost of a silent failure is highest, but the techniques transfer to any agent.

Why agent bugs look different from normal bugs

A traditional bug is deterministic: the same input produces the same wrong output, so you can attach a debugger and walk the stack. An agent bug is probabilistic and emergent. The model makes a decision at each turn based on the full conversation history, and a single bad decision early on poisons every turn after it. By the time you notice the symptom — a stuck run, a $40 invoice, a wrong conclusion — the root cause is buried twenty messages back in a transcript you never saved.

The first rule of debugging Claude agents, therefore, is to make the run observable before you make it smart. Log every model turn, every tool call with its full arguments, every tool result, and the token counts for each. Without that transcript you are guessing. With it, almost every failure becomes obvious in hindsight. Agent debugging is mostly transcript archaeology.

Failure mode one: the runaway loop

The loop is the most expensive failure and the easiest to detect once you know its shape. The agent calls a tool, gets a result it does not consider sufficient, and calls the same tool — or a tight cycle of two tools — again and again without making progress. In a security triage agent this often looks like repeatedly querying a threat-intel API for an IP that returns "unknown," with the model unable to accept that "unknown" is the final answer.

flowchart TD
  A["Agent turn"] --> B{"Loop guard: same tool+args\nseen 3x?"}
  B -->|No| C["Execute tool call"]
  C --> D{"Progress made?\n(new info in result)"}
  D -->|Yes| A
  D -->|No| E["Increment no-progress counter"]
  E --> A
  B -->|Yes| F["Break loop: inject\n'tool exhausted' message"]
  F --> G["Force final answer or escalate"]

The fix has two layers. First, a hard turn budget: cap any single agent run at, say, twenty-five turns, and treat hitting the cap as an error to investigate, not a number to raise. Second, a semantic loop guard. Hash each tool call by name plus normalized arguments, keep a small window of recent hashes, and if the same call appears three times, stop calling the model blindly. Instead inject a system-level message — "This tool has returned the same result repeatedly; you must now answer with the information you have or escalate to a human" — which usually unsticks the model in one turn.

With Claude Code and the Agent SDK you can implement this in a hook or a tool-call middleware that runs before each execution. The model never sees your counter; it just sees a nudge at the right moment. The most common mistake here is putting the loop guard only in the harness and not telling the model what changed, which leaves Claude confused about why its tool suddenly stopped working.

Failure mode two: the wrong tool call

Wrong-tool errors come from tool definitions that overlap or describe themselves poorly. If you give an agent both search_logs and query_siem with vague descriptions, Claude will sometimes pick the one that cannot answer the question, get an empty result, and either give up or loop. The model is not careless — it is reasoning from your descriptions, and your descriptions are ambiguous.

The cure is to treat tool descriptions as a first-class part of the prompt. Each tool should state in plain language exactly when to use it and, crucially, when not to. A good description reads: "Use this to search raw firewall logs by source IP within the last 7 days. Do not use for historical analysis beyond 7 days; use query_siem for that." Adding the negative case dramatically reduces misrouting. When two tools genuinely overlap, merge them or add a router tool that picks the right backend itself.

To debug wrong-tool selection, build a small eval set of representative inputs paired with the tool the agent should have called first. Run it on every change to your tool definitions. When selection accuracy drops, you will usually find a description you edited that introduced ambiguity. This turns a fuzzy problem into a metric you can watch.

Failure mode three: hallucinated arguments

The subtlest failure is the well-formed tool call with a fabricated argument. The agent calls block_ip with an address that appears nowhere in the conversation, or passes a date range it inferred rather than read. The call succeeds at the protocol level — the schema validates — so nothing errors, and the wrong action executes. In a defensive context this is the failure that blocks a customer's IP or quarantines the wrong host.

Schema validation alone will not save you, because a hallucinated IP is still a valid string. The defense is provenance checking at the harness layer: before executing a high-impact tool, verify that the key arguments actually appear in the prior tool results or the user input. If block_ip is asked to block an address the agent never retrieved, reject the call and return a message telling Claude to ground the argument in real data. You can also lower hallucination rates upstream by instructing the model, in the system prompt, to quote the source of any identifier it acts on.

The broader principle is that any tool which changes state in the real world deserves a confirmation gate — automated provenance checks for low-risk actions, an actual human approval for destructive ones. Agentic systems should be fast on reads and deliberate on writes.

A repeatable debugging loop

Put these together and you get a workflow. Capture full transcripts. When a run misbehaves, replay the transcript and find the first bad turn — not the symptom turn. Classify it as loop, wrong tool, or bad argument. Add the corresponding guard (turn budget and semantic loop guard; sharper tool descriptions; argument provenance checks). Then add that exact failing case to an eval set so it can never regress silently. Over a few weeks this converts a flaky agent into a dependable one, because every production failure becomes a permanent test.

Frequently asked questions

What causes Claude agents to get stuck in loops?

Loops almost always come from the model receiving a tool result it considers inconclusive and retrying instead of accepting it. Add a hard turn budget plus a semantic loop guard that detects repeated identical tool calls and injects a message telling the agent the tool is exhausted and it must answer or escalate.

How do I stop an agent from calling the wrong tool?

Sharpen your tool descriptions so each one states when to use it and when not to, including the negative case that points to the correct alternative tool. Maintain a small eval set mapping representative inputs to the expected first tool call, and run it whenever you change tool definitions.

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

A hallucinated argument is a tool parameter the model fabricated rather than derived from real data — for example a block_ip call with an address that never appeared in the conversation. Catch it with a provenance check at the harness layer that verifies key arguments exist in prior inputs or tool results before executing high-impact tools.

Do I need a separate observability stack for agents?

You need structured logging of every model turn, tool call, tool result, and token count, plus the ability to replay a full transcript. Many teams start with simple JSONL logs and a replay script before adopting a dedicated tracing tool; the key is that no production run should be un-replayable.

Bringing agentic AI to your phone lines

The same loop guards, tool-routing discipline, and argument-grounding checks that keep a security agent honest are what keep a voice agent honest. CallSphere applies these agentic-AI patterns to voice and chat — multi-agent assistants that answer every call, use tools mid-conversation, and book work around the clock without wandering off task. See it live 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.