Debugging Claude Code in Large Codebases: Failure Modes

The first time you point Claude Code at a million-line monorepo, it usually does something impressive: it finds the right file, reads the surrounding code, and proposes a clean patch. The second time, it might spend four minutes re-reading the same directory, call a search tool with a malformed argument, and confidently reference a function that does not exist. Both behaviors come from the same engine. Understanding why the second one happens — and how to engineer around it — is the difference between an agent that ships and one your team quietly stops trusting.

This post is a field guide to the three failure modes that dominate large-codebase work: runaway loops, wrong tool calls, and hallucinated arguments. For each, I'll explain the underlying mechanism, show how it surfaces, and give you the specific levers Claude Code exposes to prevent it.

Why big repos make agents fail differently

In a small project, the entire relevant context fits comfortably inside the model's working set. Claude can hold the file, its imports, and the test file in mind at once, so its tool calls stay grounded. A large codebase breaks that comfort. The agent is now navigating partial views: a grep result here, a 200-line slice of a 4,000-line file there, a directory listing that scrolled three turns ago. Each tool result is a lossy snapshot, and the model is stitching them into a mental map it can never fully see.

Failure modes are simply what happens when that map drifts from reality. A loop is the agent re-fetching context it already has because it lost track of having seen it. A wrong tool call is the agent reaching for grep when it needed the language server. A hallucinated argument is the agent filling a required parameter with a plausible-but-fictional value because the real one fell out of context. None of these are random — they are predictable consequences of bounded attention over an unbounded repo.

Failure mode one: the runaway loop

Loops are the most visible failure and the most demoralizing to watch. The classic shape: Claude reads config.ts, decides it needs the schema, reads schema.ts, realizes the schema imports from config.ts, and reads config.ts again. Twenty turns later it is still circling, burning tokens, no closer to a patch.

flowchart TD
  A["Agent turn starts"] --> B{"Have I made progress\nsince last checkpoint?"}
  B -->|Yes| C["Take next action"]
  B -->|No| D{"Repeated tool call\nor same file re-read?"}
  D -->|Yes| E["Inject summary +\nforce a plan step"]
  D -->|No| C
  E --> F{"Loop count > threshold?"}
  F -->|Yes| G["Halt & ask human"]
  F -->|No| C
  C --> A

The mechanism is loss of state. Each turn, the model re-derives what to do from the conversation so far, and if that history no longer clearly encodes "I already have the schema," it re-fetches. The fix is to make progress legible. Ask Claude to maintain a short running plan it updates each step — a scratchpad of "done / doing / next." When the plan is in context, the model checks it against intended actions and stops repeating itself. Hooks help here too: a stop hook that detects N identical tool calls in a row can inject a message forcing a re-plan or hand control back to you.

Subagents are the other structural defense. Instead of one long-running agent accumulating a tangled history, an orchestrator spawns a focused subagent for "map the auth module," gets back a clean summary, and discards the noisy intermediate turns. The orchestrator's context stays crisp, which is exactly the condition under which loops do not form.

Failure mode two: the wrong tool call

Claude Code in a real repo has many ways to find code: shell grep, a file reader, a language-server MCP server, project-specific skills. Wrong-tool failures happen when the model reaches for a blunt instrument when a precise one exists. It greps for a symbol name and gets 300 hits across test fixtures and vendored code, when a "find references" call would have returned the four real call sites.

Two things drive this. First, tool descriptions that are vague or overlapping. If your grep tool and your semantic-search tool both say "search the codebase," the model has no basis to choose well. Sharpen the descriptions: state exactly when each is the right pick and what it is bad at. Second, missing affordances. If you have not wired up a language-server MCP server, the agent cannot call "find references" — it will fall back to grep because that is all it has. In large codebases, giving Claude structured navigation tools (definitions, references, type info) does more to prevent wrong-tool calls than any amount of prompting.

A practical tactic: encode tool-selection rules as a skill. A short skill that says "for symbol lookups prefer the LSP server; use grep only for free-text or comments" loads exactly when navigation is relevant and steers the choice without bloating every prompt.

Failure mode three: hallucinated arguments

This is the quietest and most dangerous failure. The agent calls a tool with a syntactically valid but factually wrong argument: a file path that does not exist, a function signature it half-remembers, an env var name it invented. Because the call succeeds at the protocol level, nothing errors loudly — the agent just acts on fiction.

Hallucinated arguments are almost always a context problem. The real value was visible 15 turns ago and has since scrolled out, so the model reconstructs it from pattern-matching. The fixes are about keeping ground truth close to the moment of use. Prefer tools that return validation errors over tools that fail silently — a write tool that rejects a non-existent path forces a correction loop. Use schema-constrained tool definitions so the model cannot supply a malformed shape. And re-ground before high-stakes actions: have the agent re-read the exact file or run a quick existence check immediately before editing, rather than trusting a memory of it.

Building a debugging loop you can trust

Debugging an agent is debugging a system, not a single prediction. Turn on verbose logging so you can see the actual tool calls and arguments — most "the model is dumb" complaints dissolve once you watch the trace and realize it was working from stale context. Add hooks that enforce invariants: block writes outside the working tree, cap consecutive identical calls, require a test run after edits. And keep runs short and checkpointed; a 60-step agent is far harder to debug than six 10-step agents that each hand off a clean summary.

The throughline across all three failure modes is context hygiene. Loops, wrong tools, and hallucinated args are different symptoms of the same disease: the agent's working picture has drifted from the repo's reality. Engineer for fresh, legible, validated context and the symptoms recede together.

Frequently asked questions

Why does Claude Code keep re-reading the same files?

It has lost track, within its current context, of having already read them. The earlier read scrolled out of the working window or was buried under noisy tool output, so the model re-derives the need to fetch it. A running plan scratchpad and shorter, subagent-scoped tasks keep that state legible and stop the re-reading.

What is a hallucinated tool argument?

A hallucinated tool argument is a tool-call parameter the model fabricates — a file path, function name, or identifier that looks plausible but does not actually exist — usually because the real value fell out of context. Schema validation, fail-loud tools, and re-grounding right before the call are the standard defenses.

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

Make tool descriptions sharp and non-overlapping, give the agent precise navigation tools (a language-server MCP server) so it isn't forced to misuse grep, and encode selection rules in a skill that loads when navigation is relevant.

Are subagents worth it just for debugging?

Yes. Spawning focused subagents that return clean summaries keeps the orchestrator's context uncluttered, which is the single biggest structural defense against loops and hallucinated arguments in large codebases.

Bringing agentic AI to your phone lines

The same context-hygiene discipline that keeps a coding agent honest is what makes a voice agent reliable mid-call. CallSphere applies these agentic patterns to voice and chat — assistants that answer every call, use tools without hallucinating, and book real 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.