Debugging Claude Agents: Loops, Bad Tool Calls, Bad Args (Building AI Agents For Startups)

The first agent demo always works. You wire Claude up to three tools, ask it to do something useful, and it nails the happy path on stage. Then you ship it, real users arrive with messy requests, and the same agent gets stuck calling the same search tool eleven times in a row, or confidently passes a customer ID into a field that wants an email address. For an early-stage startup, that gap between demo and production is where the credibility — and the runway — gets burned. This post is about closing it: the specific ways Claude-based agents fail, and how to debug each one without guessing.

Debugging an agent is not like debugging a function. There is no stack trace pointing at line 42. The behavior emerges from a model's choices over many turns, so the bug lives in the trajectory — the ordered sequence of thoughts, tool calls, results, and follow-ups. If you can see the trajectory clearly, most agent bugs become obvious. If you can't, you are debugging blind. So the very first thing to build, before any clever fixes, is observability.

Why agents fail differently than code

A conventional bug is deterministic: same input, same wrong output, every time. Agent bugs are probabilistic and context-dependent. The same prompt can succeed at temperature defaults nine times and fail the tenth because the model latched onto an ambiguous tool description. That means reproduction is itself a skill. When a user reports "the agent did something weird," you need the full message list — system prompt, every tool definition, every tool result, and the model's outputs — captured at the moment it happened, because re-running may not reproduce it.

The three failure modes that dominate startup agents are loops, wrong tool selection, and hallucinated arguments. They look different on the surface but share a root cause: the model is working from an incomplete or contradictory picture of its tools and state. Loops happen when a tool returns something the model can't act on, so it tries again. Wrong tool calls happen when two tools have overlapping descriptions. Hallucinated arguments happen when a required parameter isn't actually available in context, so the model invents a plausible-looking value rather than asking.

Reading the trajectory

Before you touch the prompt, instrument the run. Log every turn as a structured record: the role, the text or tool-use block, the tool name, the exact arguments, and the raw tool result. With Claude this is straightforward because tool use is explicit in the API — each assistant turn either produces text or one or more tool_use blocks, and you feed back matching tool_result blocks. Capture all of it. The flowchart below shows the decision path I walk every time an agent misbehaves.

flowchart TD
  A["Agent misbehaved"] --> B{"Same tool called 3+ times?"}
  B -->|Yes| C["Loop: inspect tool_result for hidden error"]
  B -->|No| D{"Right tool for the goal?"}
  D -->|No| E["Selection bug: sharpen tool descriptions"]
  D -->|Yes| F{"Args valid & grounded?"}
  F -->|No| G["Hallucinated arg: make field required or ask-first"]
  F -->|Yes| H["Logic gap: fix system prompt or add guardrail"]
  C --> I["Re-run with fixed tool contract"]
  E --> I
  G --> I

That single branch — "same tool called three or more times" — catches the most expensive bug class first, because loops are what blow up your token bill and your latency. When you see one, do not assume the model is confused. Read the tool result it received. Nine times out of ten the tool returned an error string, an empty array, or a 200 response with a buried "status":"failed" field, and the model could not tell that the call was unsuccessful.

Fixing loops at the tool boundary

Loops are almost always a contract problem, not a reasoning problem. If your search_orders tool returns an empty list when nothing matches, the model has no way to distinguish "no results" from "I phrased the query wrong, let me retry." The fix is to make tool results self-describing. Return an explicit signal: {"status":"ok","results":[],"message":"No orders found for that customer. Ask the user for an order number or email."}. Now the model has an instruction, not a void, and it stops retrying.

Two more loop guards earn their keep in production. First, enforce a hard cap on total tool calls per run in your orchestration code, and when you hit it, inject a message telling the model to stop and summarize what it has. Second, deduplicate: if the model issues a tool call with arguments byte-identical to one already made this run, intercept it and return the cached result plus a note that this exact call was already made. That breaks the tightest loops mechanically while you fix the underlying tool contract.

Wrong tool, wrong args

Tool selection errors trace back to descriptions. Claude chooses tools largely from their names and descriptions, so two tools described as "look up customer information" and "get customer details" are a coin flip. Make each description state precisely what the tool does, what it needs, and — critically — when not to use it: "Use to fetch a customer's billing address by account ID. Do not use for order history; use get_orders for that." Negative guidance disambiguates better than longer positive descriptions.

Hallucinated arguments are the subtlest. The model fills a required field with a value that looks right but was never given — an invented UUID, a guessed email. The defense is twofold. In the tool schema, use strict typing and patterns (for example a regex constraint on an ID format) so malformed values are rejected before they reach your API. And in the system prompt, instruct the agent explicitly: "If a required value was not provided by the user or a prior tool result, ask for it. Never invent identifiers." Pair that with a server-side validation layer that returns a clear error the model can recover from, rather than letting a fabricated argument silently corrupt data.

Building a debugging habit

The teams that ship reliable agents treat every production failure as a test case. When you fix a loop or a hallucinated argument, capture that trajectory into a regression suite and replay it against future prompt changes. Agent behavior is fragile under edits — tightening one tool description can shift selection on an unrelated task — so without a replay suite you are playing whack-a-mole. Claude's tool-use logs make this cheap: the recorded message list is the test fixture.

Debugging an AI agent is the practice of reconstructing the model's decision trajectory — the sequence of tool calls, arguments, and results — to locate where its picture of the task diverged from reality. Get observability first, classify the failure into loop, selection, or argument, and fix the tool contract before you touch the model's reasoning. Most of what looks like a smart-model problem is actually a dumb-interface problem you can fix in an afternoon.

Frequently asked questions

How do I stop a Claude agent from looping?

Make tool results self-describing so the model can tell success from failure, add a hard cap on tool calls per run in your orchestration code, and deduplicate identical calls by returning a cached result with a note. The root cause is almost always a tool that returns an ambiguous empty or error result.

Why does my agent call the wrong tool?

Overlapping tool descriptions. Claude picks tools from their names and descriptions, so rewrite each to state exactly what it does and when not to use it, including a pointer to the correct alternative. Negative guidance disambiguates more reliably than longer positive descriptions.

What causes hallucinated tool arguments?

A required field whose value isn't actually present in context. The model fills it with a plausible guess rather than asking. Fix it by instructing the agent to ask for missing identifiers instead of inventing them, and by enforcing strict schema validation server-side so fabricated values are rejected with a recoverable error.

Do I need a special tool to debug agents?

No. Start with structured logging of every turn — system prompt, tool definitions, tool calls with exact arguments, and raw results. Claude's tool use is explicit in the API, so that message list is both your debugger and your regression fixture. Add tracing tools later once you know what you're looking for.

Bring agentic reliability to your phone lines

CallSphere takes these same debugging disciplines — trajectory logging, loop guards, and grounded tool calls — and applies them to voice and chat agents that handle real customer conversations, call tools mid-call, and book work around the clock. See how it holds up under live traffic 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.