Debugging Claude Agents: Loops, Bad Tool Calls, Fixes

An agent that works in your demo and falls apart in production is the rite of passage for every team building on Claude. The model reasons well, the tools are wired up, the first ten runs look magical — and then a real user hits an edge case and the agent spins in a loop calling the same MCP tool nine times, or invents a customer_id that never existed, or fires a delete when you only asked it to read. Debugging agents is different from debugging ordinary code because the control flow is generated at runtime by a model, not written by you. You can't set a breakpoint inside a thought. What you can do is make the agent's reasoning and actions observable, then attack each failure mode with a specific fix.

This post walks through the three failure modes that account for most agentic bugs — runaway loops, wrong tool calls, and hallucinated arguments — and the concrete techniques that make them go away. Everything here assumes a Claude-based stack: Claude Code or the Claude Agent SDK driving a loop of model turns and tool calls over MCP servers, with skills loaded dynamically.

Why agent bugs hide until production

Traditional software fails the same way every time given the same input. Agents don't. The model samples, the context window fills differently on each run, and a tool that returned clean JSON yesterday returns an error string today. That non-determinism means a bug can sit dormant for a hundred runs and then surface when an upstream API times out or a user phrases a request in a way your prompt never anticipated. The first discipline of agent debugging is therefore reproducibility: capture the full trajectory of every run — system prompt, every tool definition in scope, each tool call with its exact arguments, each tool result, and the model's text between calls.

Without that trace you are guessing. With it, you can replay a failing run, diff it against a passing one, and see the exact turn where reality diverged from intent. Most teams that struggle with flaky agents simply aren't logging at the tool-call granularity. Start there before you touch the prompt.

Failure mode one: the runaway loop

Loops are the most common and most expensive failure. The agent calls a tool, the result isn't quite what it expected, so it calls the same tool again with a tiny variation, gets the same unsatisfying result, and repeats until you hit a turn limit or burn your token budget. Loops usually trace to one of three causes: a tool that returns an ambiguous or empty result with no clear signal of failure, a goal the agent can't actually satisfy with the tools it has, or missing memory of what it already tried.

flowchart TD
  A["Model turn"] --> B{"Tool call requested?"}
  B -->|No| C["Return final answer"]
  B -->|Yes| D["Execute tool"]
  D --> E{"Same call seen before?"}
  E -->|Yes, 2nd+ time| F["Inject 'you already tried this' note"]
  E -->|No| G["Append result to context"]
  F --> H{"Turn or cost cap hit?"}
  G --> H
  H -->|Yes| I["Halt & escalate to human"]
  H -->|No| A

The fix is layered. First, give every tool a result schema that distinguishes success, empty, and error unambiguously — an empty search should return {"results": [], "status": "no_match"}, not an empty string the model interprets as a transient glitch. Second, track a hash of recent tool calls and, when the agent repeats one, inject a system note like "You already called search with these arguments and got no results; try a different approach or stop." Third, always enforce a hard turn cap and a token-cost cap that halts the run and escalates rather than looping forever. Claude is good at taking a hint that it's stuck — the trick is to actually give it one.

Failure mode two: the wrong tool call

Sometimes the agent picks a plausible but incorrect tool — it calls update_record when the user asked a question that only needed get_record, or it reaches for a generic web search when a purpose-built internal tool exists. This is almost always a tool-description problem, not a model problem. The model chooses tools by reading their descriptions, so vague, overlapping, or misleadingly named tools cause mis-selection.

Treat tool descriptions as prompt engineering. Each tool's description should state precisely what it does, when to use it, when not to use it, and what it returns. If two tools have overlapping purposes, either merge them or sharpen the boundary in the descriptions ("Use search_orders for historical orders; use get_live_order only for orders placed in the last hour"). Reducing the number of tools in scope helps too — an agent with eight well-chosen tools makes better decisions than one with forty. Agent Skills help here by loading the right tool guidance only when the task calls for it, keeping the active tool surface small.

Failure mode three: hallucinated arguments

The third mode is subtle: the agent calls the right tool but fabricates an argument. It passes order_id: "ORD-00000" when it never saw a real order id, or it guesses a date format the API rejects. Hallucinated arguments come from the model trying to satisfy a tool's required schema when it lacks the real value. The structural fix is to make tools fail loudly and informatively: validate inputs server-side and return an error that names the problem ("order_id 'ORD-00000' not found; call list_orders first to get a valid id"). A good error message turns a hallucination into a self-correcting step.

Strong JSON-schema definitions on your tool inputs also reduce this — tight enums, format constraints, and required fields give Claude less room to improvise. And in your system prompt, instruct the agent explicitly: never invent identifiers; if you don't have a value, use a lookup tool to obtain it. The combination of strict schemas, informative validation errors, and an explicit no-fabrication instruction eliminates most argument hallucinations.

Building a debugging workflow that scales

Ad-hoc debugging doesn't scale past a handful of agents. Bake observability into the framework: structured trace logging on by default, a replay harness that re-runs a captured trajectory against the current prompt and tools, and a small library of "known bad" trajectories you regression-test against. When a new failure appears in production, capture it, reproduce it locally, add it to the regression set, fix it, and confirm the fix doesn't break a previously passing case. This is the same red-green discipline as ordinary testing, applied to non-deterministic runs.

One more practice pays off disproportionately: have Claude help debug Claude. Feed a failing trajectory back to the model and ask it to explain why it made each tool call and what would have helped it choose better. The model is often startlingly accurate about its own missteps, and its answers point straight at the prompt or tool-description fix you need.

Frequently asked questions

What is the most common cause of Claude agents getting stuck in loops?

The most common cause is a tool that returns ambiguous results — an empty or error response the model reads as a temporary glitch rather than a definitive answer. The agent retries hoping for a different outcome. Fix it by returning explicit status fields, tracking repeated calls, and capping total turns.

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

Sharpen your tool descriptions. Tool selection is driven entirely by the description text, so state clearly when to use and not use each tool, eliminate overlapping tools, and keep the number of tools in scope small. Loading tool guidance through Agent Skills only when relevant keeps the active set focused.

Why does my agent invent fake IDs and arguments?

It fabricates arguments when a tool's schema requires a value the model doesn't actually have. Add strict JSON-schema constraints, validate inputs server-side, return errors that name the missing value and the lookup tool to get it, and instruct the model never to invent identifiers.

Should I log every tool call in production?

Yes. Tool-call-level tracing — arguments, results, and the model's reasoning between calls — is the single highest-leverage investment in agent reliability. Without it you can't reproduce or diagnose the non-deterministic failures that define agentic systems.

Bringing agentic AI to your phone lines

The same debugging discipline — observable trajectories, loop guards, and self-correcting tool errors — is what keeps a voice agent reliable on a live call. CallSphere builds multi-agent voice and chat assistants that answer every call, use tools mid-conversation, and book work around the clock. See it live at callsphere.ai.