Debugging Claude Code Agents: Loops, Bad Tool Calls, Fixes

An agent that works in the demo and falls apart in production is the most common story in agentic engineering. You wire up a Claude Code agent with a handful of MCP tools, it nails the happy path, and then a week later it is stuck calling the same tool nine times in a row, or it passes a directory path where a file path belongs, or it confidently invokes a function that does not exist. None of these are mysterious model failures. They are debuggable behaviors with traceable root causes, and the teams who ship reliable agents are simply the ones who learned to read the trace.

This post is a working field guide to the three failure modes that account for the overwhelming majority of agent bugs — loops, wrong tool calls, and hallucinated arguments — and the specific instrumentation and prompt-level fixes that resolve each one. The through-line is that you cannot fix what you cannot see, so the first half of debugging any agent is making its decision process legible.

Why agentic loops happen and how to break them

The classic loop looks like this: Claude reads a file, decides it needs more context, lists a directory, reads the same file again, lists the directory again, and never makes forward progress. The naive explanation is that the model is "confused," but the precise cause is almost always that the agent's state is not changing between turns. Each turn produces an observation that looks identical to the last, so the model — reasoning correctly given what it sees — picks the same next action.

The fix is to make progress observable. After a tool returns, append a compact summary of what changed and what is now known, so the next turn has different input. A second cause is a missing terminal condition: the agent has no clear notion of "done," so it keeps searching for more. Give it an explicit success contract in the system prompt — "stop and report once you have produced a passing test" — and the loop usually disappears. A third cause is a tool that silently no-ops; if a write fails but returns success, the agent re-reads, sees no change, and tries again forever.

Instrumenting the agent so failures become readable

Before you touch the prompt, build the trace. Every debugging session should start from a structured log of each turn: the model's stated reasoning, the tool it chose, the exact arguments it passed, the raw tool result, and a token count. With that record you can replay any run and see precisely where intent diverged from action.

flowchart TD
  A["Agent turn starts"] --> B{"Did state change
since last turn?"}
  B -->|No| C["Loop risk: inject progress
summary & recheck terminal condition"]
  B -->|Yes| D{"Tool call valid?"}
  D -->|Schema mismatch| E["Wrong-tool fix:
tighten tool descriptions"]
  D -->|Bad arguments| F["Hallucinated-arg fix:
validate & return error to model"]
  D -->|Valid| G["Execute tool"]
  G --> H["Log reasoning, args,
result & tokens"]
  H --> I{"Success contract met?"}
  I -->|No| A
  I -->|Yes| J["Report & stop"]

The diagram captures the discipline: every turn passes through state-change and validity gates before the tool runs, and every result is logged. This is not heavyweight tooling — a JSON line per turn is enough — but it converts "the agent is acting weird" into "on turn 7 it passed a glob pattern to a tool expecting a literal path."

Wrong tool calls: a description problem, not a model problem

When Claude reaches for the wrong tool, the instinct is to blame the model. Look at your tool definitions first. Claude chooses tools from their names and descriptions, so two tools with overlapping descriptions create a coin flip. If search_files and grep_code both say "find things in the codebase," the model has no principled basis to pick correctly. The fix is to write descriptions that draw sharp boundaries: state what each tool is for, what it is not for, and when to prefer the other.

A related cause is tool sprawl. An agent handed forty tools makes worse choices than one handed eight, because the decision space is larger and the relevant tool is buried. Curate aggressively. If two tools nearly always get called together, consider merging them. And give examples in the description — "use this to read a single known file path; use list_dir first if you do not know the path" — because concrete usage guidance steers selection far better than abstract prose.

Hallucinated arguments and how validation closes the loop

Hallucinated arguments are the most fixable failure of all, because the agent loop gives you a place to intervene. When Claude invents a parameter, passes a nonexistent enum value, or fabricates an ID, your tool layer should validate the call against the schema and, on failure, return a clear error message back to the model rather than crashing or silently coercing. "Error: status must be one of open, closed, pending — received 'in_progress'" lets the next turn self-correct. This turns a hard failure into a soft, recoverable one.

The deeper fix is to constrain what can be hallucinated in the first place. Prefer enums over free-text fields, require IDs the agent has actually seen in a prior tool result rather than ones it must guess, and keep argument schemas tight. A definition worth quoting: an agentic failure mode is a recurring, diagnosable pattern in which an autonomous agent's tool-use behavior diverges from the task goal in a way that is traceable to its inputs, tool definitions, or stopping conditions. Naming the mode is the first step to fixing it.

A repeatable debugging workflow

Put it together into a routine. First, reproduce with a fixed seed of inputs and capture the full trace. Second, find the first turn where behavior diverged — not the symptom turn, the origin turn. Third, classify it: is it a loop (state not changing), a wrong tool (ambiguous descriptions), or a bad argument (loose schema)? Fourth, apply the targeted fix and re-run the same trace to confirm. Resist the urge to fix everything in the prompt at once; change one thing, observe, repeat.

Most importantly, build a small regression set of the traces that previously broke. Every bug you fix becomes a test case, so the next prompt change cannot silently reintroduce it. Agentic debugging is not a dark art; it is observability plus disciplined isolation, the same skills that make any complex system maintainable.

Frequently asked questions

Why does my Claude agent keep calling the same tool repeatedly?

Almost always because its observable state is not changing between turns — the tool result looks identical, so the model picks the identical next action. Inject a progress summary after each turn and verify the tool actually mutates state and reports failures honestly, rather than silently no-op-ing.

How do I stop Claude from choosing the wrong tool?

Treat it as a tool-description problem. Write sharp, non-overlapping descriptions that say what each tool is and is not for, add a usage example, and reduce the total number of tools so the relevant one is not buried among near-duplicates.

What should happen when an agent passes invalid arguments?

Validate against the tool schema and return a descriptive error back to the model instead of crashing or coercing the value. A message like "expected one of [a, b, c], got x" lets the next turn correct itself, converting a hard failure into a recoverable one.

Do I need special tooling to debug agents?

No. A structured per-turn log capturing reasoning, tool name, arguments, raw result, and token count is enough to replay any run and locate the turn where intent diverged from action. Build the trace before changing the prompt.

Bringing agentic AI to your phone lines

The same trace-first debugging discipline keeps real conversations reliable. CallSphere applies these agentic-AI patterns to voice and chat — assistants that answer every call, use tools mid-conversation, and never get stuck in a loop with a caller. 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.