Debugging Claude Code: Loops, Bad Tool Calls, Fixes (Claude Code Session 1M Context)

The first time a Claude Code session goes wrong, it rarely looks like a crash. It looks like progress that quietly stops being progress. The agent reads a file, edits it, runs the test, sees the same failure, reads the file again, edits it again, runs the same test again — and you realize twenty minutes later that you have been watching a very polite infinite loop. Long-running agentic sessions, especially ones that stretch toward the 1M-token context window, have their own catalog of failure modes, and debugging them is a distinct skill from debugging ordinary code.

This post is a practical guide to the three failures you will hit most: loops (the agent repeats an action that isn't moving the task forward), wrong tool calls (it picks a valid tool for the wrong reason), and hallucinated arguments (it calls a real tool with parameters it invented). For each, the goal is the same — find the signal in the transcript, then change the environment so the agent can't make the same mistake again.

Why agentic loops happen and how to spot them

A loop is the most demoralizing failure because every individual step looks reasonable. The agent has a goal, it takes an action it believes advances the goal, it observes a result, and it tries again. The problem is that the observation never changes in a way that updates its plan. Usually the root cause is a feedback signal that is ambiguous: a test that fails with the same opaque error, a linter whose message doesn't say which line, or a tool that returns an empty result that the model interprets as "try once more."

The tell-tale sign in a transcript is a repeating triple — same tool, same arguments, same observation — appearing three or more times with no edit in between that meaningfully changes state. When you review a session, scan for that rhythm before you read any prose. The model's reasoning text will sound confident and varied even while the underlying actions are identical, so trust the action stream over the narration.

The fix is almost never "tell the model to stop looping." It is to make the feedback richer. Give the failing test a clearer assertion message. Wrap the flaky command so its output includes the exact file and line. Add a hook that detects N identical tool calls in a row and injects a system note that says, in effect, "this approach has failed three times; step back and form a new hypothesis." That nudge changes the observation, which is the only thing that breaks a loop.

flowchart TD
  A["Agent action"] --> B["Observe result"]
  B --> C{"Same action+result\n3x in a row?"}
  C -->|No| D["Continue normally"]
  C -->|Yes| E["Loop detected by hook"]
  E --> F["Inject 'change hypothesis' note"]
  F --> G{"New approach taken?"}
  G -->|Yes| D
  G -->|No| H["Halt & ask human"]

Wrong tool calls: right syntax, wrong intent

A wrong tool call is when Claude selects a tool that exists and invokes it correctly, but it is the wrong tool for the situation. It runs a broad search when it should have read a known file. It restarts a service when it only needed to read a log. The call succeeds, so nothing errors — the damage is wasted turns or, worse, a side effect you didn't want.

These errors trace back to tool descriptions that overlap or under-specify. If two tools could plausibly answer the same request, the model has to guess, and it guesses based on whatever phrasing is closest in its context. The fix is to write tool descriptions that say not just what the tool does but precisely when to prefer it and when not to. A description like "Search the codebase. Prefer Read when you already know the file path; use this only when the location is unknown" removes a whole class of wrong choices.

For destructive or expensive tools, don't rely on description alone. Gate them. A pre-execution hook that requires confirmation for anything matching a deploy, delete, or restart pattern turns a silent wrong call into a visible decision point. In long sessions this matters more, because by turn eighty the original instructions are far back in the context and the model is leaning on recent momentum.

Hallucinated arguments and how to validate them

The third failure is the sneakiest: the agent calls a real tool with arguments that don't correspond to anything real. It passes a file path that doesn't exist, a function name it misremembered, or a flag the CLI never had. Sometimes the tool errors immediately, which is the lucky case. Sometimes the argument is plausible enough to succeed against the wrong target.

Hallucinated arguments cluster around facts the model hasn't recently verified. If the agent hasn't listed a directory in forty turns, its memory of which files exist has gone stale, and it will confidently reference a path that was renamed. The defense is to validate arguments at the boundary, not in the prompt. Your tool wrapper should check that paths exist, that enum values are in range, and that referenced symbols can be resolved — and on failure, return an error message that tells the model exactly what was wrong and what valid options look like. A good error turns a hallucination into a self-correcting step.

It also helps to keep the agent's world model fresh. Encourage a pattern where, before a batch of edits, the agent re-reads the relevant files or re-lists the directory. This costs a few tokens but prevents the expensive failure of editing a file that no longer matches its mental copy.

Reading the transcript like a debugger

The transcript is your stack trace. Treat it as one. When a session goes sideways, don't start by re-reading the agent's reasoning — start by extracting the sequence of tool calls and their results, stripped of narration. Patterns jump out instantly: the same grep five times, a write followed by a read that shows the write didn't take, a tool call whose result was an error the agent ignored. The single most common root cause of a derailed session is an ignored error several turns earlier that the agent narrated past.

Once you find the divergence point, ask a different question than you would for ordinary code. Not "what's the bug in this line" but "what did the environment fail to tell the agent." Almost every agentic failure is, at bottom, an observability failure: the model lacked the signal it needed to choose correctly. Fix the signal and the same prompt usually succeeds on the next run.

Designing sessions that fail loudly

The best debugging strategy is to make failures loud early. Set explicit success criteria the agent can check itself — a specific test that must pass, a specific output file that must exist. Add checkpoints so a long task is a series of verifiable milestones rather than one opaque march. And cap the blast radius: run the agent against a sandboxed copy, a branch, or a container, so that even a confidently wrong action is recoverable.

When you combine clear self-verifiable goals, rich tool errors, loop detection, and a recoverable environment, the failure modes don't disappear — but they surface as small, legible events instead of silent twenty-minute detours. That is the difference between an agent you have to babysit and one you can trust to run.

Frequently asked questions

What is the most common Claude Code failure mode?

Silent loops are the most common derailment in long sessions: the agent repeats a tool call with the same arguments because the observation it gets back never changes meaningfully. The fix is richer feedback — clearer error messages and a loop-detection hook that nudges the model to form a new hypothesis after repeated identical actions.

How do I stop the agent from calling tools with made-up arguments?

Validate arguments inside your tool wrappers rather than trusting the prompt. Check that paths exist and enum values are valid, and return a precise error listing the valid options on failure. A good error message converts a hallucinated argument into a self-correcting step on the very next turn.

Why does the agent pick the wrong tool even though the call succeeds?

Overlapping tool descriptions force the model to guess between similar options. Write descriptions that state when to prefer each tool and when not to, and gate destructive tools behind a confirmation hook so a wrong choice becomes a visible decision instead of a silent side effect.

Where do I start when a long session goes off the rails?

Extract just the sequence of tool calls and results, ignoring the model's narration, and look for the first ignored error or the first repeating triple. That divergence point is almost always an observability gap — the environment failed to give the agent the signal it needed to choose correctly.

Bringing agentic AI to your phone lines

The same debugging discipline — loud failures, validated tool inputs, and recoverable environments — is what makes CallSphere's voice and chat agents dependable: multi-agent assistants that answer every call and message, 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.