Debugging Claude Code agents: loops, bad tool calls (Non Technical PM Ships App)

Six weeks into shipping my first app with Claude Code, I had a working product and a recurring nightmare: the agent that built features beautifully on Monday would, on Wednesday, get stuck calling the same search tool eleven times in a row before giving up. I am not an engineer by training — I am a product manager who decided to learn by doing — and nothing humbled me faster than watching an autonomous agent fail in slow motion. The good news is that agent failures are not random. They cluster into a small number of recognizable shapes, and once you can name them you can fix them.

The three failure modes you will actually hit

Most of the trouble I encountered fell into one of three buckets. The first is the loop: the agent repeats a near-identical action and never converges. The second is the wrong tool call: the agent reaches for a tool that exists but is not the right one for the step, like running a file search when it should have read a file it already knows the path to. The third is the hallucinated argument: the tool is correct but the parameters are invented — a directory that does not exist, a column name the schema never had, an ID it never observed.

What makes these worth distinguishing is that each has a different root cause and therefore a different remedy. Loops are usually a feedback problem: the agent is not getting a signal that its last action changed anything, so it tries again. Wrong tool calls are usually a context problem: the tool descriptions are vague or overlapping, so the model picks plausibly rather than correctly. Hallucinated arguments are usually a grounding problem: the agent is filling a required field from its prior, not from anything it actually retrieved in this run.

Debugging an agent is the practice of reading the full trace of its reasoning, tool calls, and tool results to locate the exact step where intent and action diverged. That sentence sounds obvious, but the discipline behind it — always read the trace, never guess from the final output — is what separates a five-minute fix from an afternoon of flailing.

Reading the trace before you touch anything

Claude Code makes the full reasoning-and-tool-call trace visible, and that transcript is the single most valuable debugging artifact you have. Before changing a prompt or a tool, I learned to scroll back and find the precise turn where things went sideways. Was the tool result empty? Did the agent misread a result that was actually fine? Did it pass an argument that no prior step had produced? The answer tells you which of the three modes you are in.

A concrete example: my app had a tool that queried a small Postgres table. The agent kept calling it with a column named customer_id that did not exist — the real column was client_id. That is a textbook hallucinated argument. The fix was not a smarter prompt; it was giving the agent a way to see the schema first, so the argument was grounded in an observed fact rather than a guess.

flowchart TD
  A["Agent run fails"] --> B["Read the full trace"]
  B --> C{"Same action repeated?"}
  C -->|Yes| D["Loop: add a stop signal & progress check"]
  C -->|No| E{"Tool wrong for the step?"}
  E -->|Yes| F["Tighten tool descriptions & reduce overlap"]
  E -->|No| G{"Args invented?"}
  G -->|Yes| H["Ground args: make the agent observe before it acts"]
  G -->|No| I["Inspect tool result quality"]

Breaking loops with stop conditions and progress signals

Loops were my most frequent failure early on, and they almost always traced back to a tool that returned the same thing whether or not the agent's situation had changed. If a search returns zero results and the agent has no instruction about what to do with zero results, retrying feels reasonable to the model. The cure has two parts. First, give the tool a clear, distinguishable failure output — an empty result should say so explicitly, not return an ambiguous blob. Second, give the agent an explicit policy: after two failed attempts at the same goal, change strategy or ask for help rather than repeat.

Claude Code's hooks are useful here. You can add a hook that watches for N identical consecutive tool calls and injects a message that breaks the pattern — effectively a circuit breaker. I also found it valuable to ask the agent, in its system instructions, to state its plan before acting and to note after each tool call whether it made progress. That tiny bit of self-reported progress tracking dramatically cut my loop rate, because the model now had to confront the fact that attempt three looked exactly like attempt two.

Fixing wrong tool calls at the description layer

When an agent picks the wrong tool, the instinct is to scold it in the prompt. That rarely works. The model is choosing from tool descriptions, and if two tools have fuzzy, overlapping descriptions, no amount of pleading fixes the ambiguity. The durable fix lives in the tool definitions. Make each description say precisely when to use the tool and, just as importantly, when not to. A description that reads "search files by name; use only when you do not already know the path — if you know the path, read it directly" eliminates an entire class of wrong calls.

This is also where Agent Skills help. A skill bundles instructions plus the right tools for a task, so when the agent loads the skill it gets a curated, non-overlapping toolset rather than the full sprawling menu. Narrowing the choice space is often more effective than improving the choice. The fewer plausible-but-wrong tools are within reach at any moment, the fewer wrong calls you get.

Grounding arguments so hallucinations stop

Hallucinated arguments are the most dangerous failure because they can succeed silently — an invented filter that happens to return rows looks like a correct result. The defense is to make the agent observe before it acts and to validate at the tool boundary. Observing means giving it a cheap way to fetch the real schema, the real file listing, the real set of valid IDs, before it constructs a call. Validating means the tool itself rejects arguments that do not match a known shape and returns a helpful error the agent can recover from.

I added a thin validation layer to every tool that touched data: unknown column, return the list of real columns; missing file, return the directory contents. That turned silent corruption into a recoverable, self-correcting loop. The agent would try a wrong argument, get told the truth, and immediately fix itself. Grounding plus good error messages is the closest thing to a universal remedy I found.

Frequently asked questions

How do I tell a loop apart from legitimate retrying?

Look at whether anything changed between attempts. Legitimate retrying follows a new piece of information — a different query, a corrected argument. A loop repeats a near-identical action with no new input and no new result. If three consecutive turns are interchangeable, it is a loop, and you need a stop condition.

Should I let the agent retry automatically or fail fast?

Allow a small, bounded number of retries — usually one or two — then fail to a different strategy or to a human. Unbounded retries burn tokens and rarely converge. The win is in changing approach after a failure, not in repeating the same approach harder.

Why does my agent invent file paths and IDs?

Because a required argument has to be filled and nothing in the current run grounded it, so the model completes it from prior knowledge. Give it a way to observe the real values first and have tools validate inputs. Hallucinated arguments almost always disappear once the agent can see ground truth before it acts.

Do better models eliminate these failures?

Stronger models like Opus 4.8 reduce them, but architecture matters more than raw capability. Clear tool descriptions, grounded arguments, and explicit stop conditions fix failures that no model upgrade alone will. Treat model choice and agent design as separate levers.

Bringing agentic AI to your phone lines

The same debugging discipline — read the trace, name the failure mode, fix it at the right layer — is what keeps voice agents reliable in production. CallSphere applies these agentic patterns to voice and chat, with assistants that answer every call, use tools mid-conversation, and recover gracefully when something goes wrong. See it live at callsphere.ai.

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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.