Debugging Claude agents: loops, bad tool calls, hallucinated args (Founders Playbook AI Native Startup)

The first time one of our Claude agents quietly burned through a few dollars of tokens overnight, it wasn't because the model was "dumb." It was stuck in a polite, well-reasoned loop: call a search tool, get an ambiguous result, decide to search again with a slightly reworded query, repeat. Every individual step looked sensible in isolation. Only the trace revealed the pathology. If you are building an AI-native startup, debugging agents is not an occasional chore — it is a core engineering discipline, and the failure modes are different from anything you debugged in a normal backend.

This post is the playbook I wish I'd had earlier: the three failure modes that account for most agent incidents, how to reproduce them, and the instrumentation that turns a mysterious run into a fixable bug.

Why agent bugs don't look like normal bugs

An agent failure mode is a recurring, undesired behavior pattern that emerges from the loop between a language model and its tools — not from a single line of broken code. That definition matters because your instincts from deterministic software mislead you here. There is no stack trace pointing at line 412. The same prompt can succeed on Monday and loop on Tuesday because the model sampled a different token, or because a tool returned data in a slightly different shape.

With Claude specifically, the agent loop is: the model receives context, decides whether to call a tool, the tool runs, its result is appended to the conversation, and the model decides again. Bugs hide in the seams of that loop. A tool that returns an empty list instead of an error, a system prompt that's ambiguous about when to stop, a result that's too large and pushes earlier instructions out of attention — each produces emergent misbehavior that no unit test on the tool alone would catch.

The three failure modes you will actually hit

Loops are the most expensive. The agent repeats a cycle without making progress: re-reading the same file, re-querying with trivial variations, or oscillating between two tools. The root cause is almost always a missing or ambiguous stop condition combined with a tool result that doesn't change the model's information state. Wrong tool calls are the most embarrassing — the agent picks delete_record when it meant archive_record, or routes a refund through the wrong API. Hallucinated arguments are the most insidious: the model invents a plausible-looking customer_id or passes a date in a format your tool never specified, and the tool either errors or, worse, silently does the wrong thing.

flowchart TD
  A["Agent run starts"] --> B{"Tool result changed state?"}
  B -->|No, same as last turn| C["Loop risk: increment repeat counter"]
  B -->|Yes| D{"Args schema-valid?"}
  C --> E{"Repeat counter > threshold?"}
  E -->|Yes| F["Break loop & escalate"]
  E -->|No| G["Allow next turn"]
  D -->|No| H["Reject call: hallucinated args"]
  D -->|Yes| I{"Tool matches intent?"}
  I -->|No| J["Wrong tool: log & correct"]
  I -->|Yes| G

The diagram captures the guardrail logic worth building into your harness from day one: detect state-stagnation for loops, validate argument schemas to catch hallucinations, and cross-check intent against the chosen tool to catch misrouting.

Make every run reproducible before you debug

You cannot debug what you cannot replay. The single highest-leverage investment is capturing the full trace of every agent run: the exact system prompt, every message, every tool call with its raw arguments, every tool result, and the model and sampling settings used. With Claude, that means logging the complete message array you sent and received, not a summarized version. When something goes wrong, you replay that trace deterministically — temperature aside — and watch where the agent's reasoning diverges from what you intended.

The Claude Agent SDK and Claude Code make this easier because the loop is structured: tool calls are explicit, typed events you can intercept and persist. Build a thin logging layer that writes each turn as a structured record. In practice I store runs as newline-delimited JSON keyed by a run ID, so any incident is one query away from a full replay. The discipline of "every run is reproducible" turns agent debugging from archaeology into engineering.

Fixing loops without breaking real progress

The naive fix for loops is a hard turn cap. It works, but it's blunt — it also kills legitimately long tasks. A better approach is progress-based termination: track a fingerprint of the agent's information state (files read, facts learned, distinct tool results seen) and break only when that fingerprint stops changing across several turns. A loop is, almost by definition, repetition without new information.

The deeper fix is in the prompt and tool design. Give the model an explicit, unambiguous stop condition and a way to signal "I'm stuck." Anthropic's guidance on agent design leans hard on letting Claude declare when it needs to escalate rather than spinning silently. Make sure your tools return informative results — an empty search should return "no results found for X; consider Y" rather than an empty array, because the empty array gives the model nothing to update on and invites a reworded retry.

Killing hallucinated arguments at the boundary

Hallucinated arguments are best stopped by your tool layer, not the model. Define strict input schemas for every tool and validate before execution. If a tool expects an ISO-8601 date and gets "next Tuesday", reject it with a precise error the model can act on: "date must be ISO-8601; received 'next Tuesday'." Claude is remarkably good at correcting itself when the error message is specific. Vague errors produce vague retries.

For high-stakes identifiers — customer IDs, account numbers — never let the model free-form them. Constrain the agent to choose from values it actually retrieved earlier in the run, or require a lookup tool before any mutating call. The pattern is: the model can only act on entities it has observed, not entities it has imagined. This single rule eliminates the most dangerous class of hallucinated-argument bugs.

Building a debugging culture, not just tooling

Tooling gets you halfway; habits get you the rest. Triage every production agent incident by failure mode — loop, wrong tool, or bad args — and tag the trace. After a few weeks you'll have a distribution, and that distribution tells you where to invest: if 70% of incidents are hallucinated args, you have a schema-validation gap, not a model problem. Keep a small library of "golden" failing traces and replay them against every prompt or tool change, so you never reintroduce a fixed bug. This is the seed of the eval loop that mature teams build around their agents.

Frequently asked questions

How do I tell a real loop from a long but legitimate task?

Track whether the agent's information state is changing. A legitimate long task keeps reading new files, calling new tools, or accumulating new facts. A loop repeats the same information state across turns. Break on stagnation, not on raw turn count, so you don't kill genuine deep work.

Why does Claude call the wrong tool even with good descriptions?

Usually because two tools have overlapping descriptions or the names don't reflect their real effect. Make tool names verb-precise (archive_record vs delete_record), keep descriptions disjoint, and add a one-line "use this when…" guidance. Ambiguity in the tool catalog is the leading cause of misrouting.

Should I lower the temperature to reduce hallucinated arguments?

Lower temperature reduces variance but doesn't fix the root cause. The durable fix is strict schema validation at the tool boundary plus constraining identifiers to observed values. Treat sampling settings as a tuning knob, not a safety mechanism.

What's the fastest way to start debugging an existing agent?

Add full-trace logging first — capture every message, tool call, and result per run. Without reproducible traces, every debugging session is guesswork. Once you can replay any run, the failure mode usually becomes obvious within minutes.

Bringing agentic AI to your phone lines

The same debugging discipline — reproducible traces, loop detection, and strict tool boundaries — is what keeps voice agents reliable in production. CallSphere applies these agentic-AI patterns to voice and chat, so AI assistants answer every call, use tools mid-conversation, and book work around the clock. 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.