Debugging Claude Agent SDK Agents: Loops, Bad Tool Calls

The first agent you build with the Claude Agent SDK almost always works in the demo and then misbehaves the moment a real user touches it. It calls the same tool nine times in a row, passes a customer ID that was never in the conversation, or quietly decides to answer a billing question by reading a file that doesn't exist. None of these are model "bugs" in the classic sense — they're emergent behaviors of a loop you wired up, and debugging them takes a different mindset than debugging a stack trace.

This post walks through the failure modes I hit most often when building agents on Claude — Opus 4.8, Sonnet 4.6, or Haiku 4.5 — and the concrete instrumentation and prompt changes that resolve them. The throughline: an agent failure is a data problem you can inspect, not a mystery you have to guess at.

Why agent bugs don't look like normal bugs

A normal program fails deterministically: the same input throws the same exception in the same place. An agent loop is a sequence of model decisions, each conditioned on the full transcript so far, so a single misstep early on poisons everything downstream. By the time you see the visible symptom — a wrong answer, a hung run — the real cause is often three turns back, in a tool result the model misread or an instruction it over-weighted.

The most useful definition to anchor on: an agentic loop is the cycle in which Claude receives context, decides whether to call a tool, the runtime executes that tool, and the result is appended to the conversation before the model is invoked again. Every bug lives in one of those four steps. Your job in debugging is to figure out which step, and the only way to do that reliably is to log the full message array — system prompt, every tool definition, every tool call with its exact arguments, and every tool result — for the run that went wrong.

The three failure modes you will hit first

Three patterns account for the large majority of early agent failures. Runaway loops: the model calls a tool, gets a result it doesn't find satisfying, and calls the same tool again with nearly identical arguments, burning turns and tokens until it hits your max-iteration cap. Wrong tool calls: the model picks search_orders when it should have picked get_order_by_id, usually because the two tool descriptions overlap. Hallucinated arguments: the model invents a parameter value — a customer ID, a date, an account number — that never appeared in the conversation, because the tool schema demanded a required field and the model would rather guess than ask.

flowchart TD
  A["Agent run starts"] --> B{"Symptom?"}
  B -->|Same tool repeated| C["Loop: check tool result clarity & stop condition"]
  B -->|Wrong tool chosen| D["Routing: dedupe overlapping tool descriptions"]
  B -->|Invented argument| E["Hallucinated arg: make field optional, add ask-first rule"]
  C --> F["Add iteration cap + dedupe guard"]
  D --> F
  E --> F
  F --> G["Re-run against transcript fixtures"]
  G --> H{"Resolved?"}
  H -->|No| B
  H -->|Yes| I["Lock in eval case"]

Notice that all three converge on the same remediation flow. That's deliberate: once you can name the failure mode from the transcript, the fix is usually mechanical.

Killing runaway loops

Loops almost always trace back to a tool result the model can't act on. If your search_orders tool returns an empty array as [] with no explanation, Claude has no signal about why it was empty — bad query? no matching records? — so it retries with a tweak. The fix is to make tool results self-describing. Instead of [], return { "results": [], "reason": "no orders found for that email in the last 90 days" }. The model now has enough to stop and report back instead of guessing again.

Defense in depth matters too. Set a hard iteration cap in your runtime so no single run can exceed, say, twelve tool calls. Layer a dedupe guard on top: if the model proposes a tool call whose name and arguments are byte-identical to one already in this run, short-circuit it and inject a synthetic result that says the call was already made and here is the prior outcome. That single guard eliminates the most expensive class of loop without touching the prompt.

Fixing wrong tool calls and hallucinated args

Wrong tool selection is a tool-design problem dressed up as a model problem. When two tools have descriptions that could plausibly answer the same request, the model is guessing — and it will guess inconsistently across runs. Audit your tool descriptions for overlap and rewrite them to be mutually exclusive: spell out exactly when to use each one, and add an explicit "do not use this for X — use Y instead" line. Claude follows these disambiguation hints closely when they're concrete.

Hallucinated arguments come from required schema fields. If get_order_by_id requires an order_id and the user only said "where's my order," the model has two options: ask a clarifying question, or fabricate an ID to satisfy the schema. Models often pick the second. Two changes fix it: make the field non-required where the workflow allows, and add a system-prompt rule like "never invent identifiers — if a required value is missing, ask the user for it before calling the tool." Pair that with input validation in the tool itself that rejects malformed IDs loudly, so a hallucinated value produces a clean, descriptive error the model can recover from rather than a silent wrong lookup.

Instrumentation that makes all of this tractable

You cannot debug what you cannot see. Before you ship any agent, build a transcript viewer that lets you replay a single run end to end: the system prompt, the tool catalog as the model saw it, and every call/result pair in order. Capture a stable run ID and the token usage per turn so you can correlate cost spikes with loop behavior. When a production run goes wrong, the first move is always to pull that transcript and read it like a play — who said what, in what order, and where the model's reasoning diverged from what you wanted.

The highest-leverage habit is to turn every confirmed bug into a fixture. Save the exact message array that triggered the failure, then add it to a regression suite that replays it against your current agent. This converts one-off debugging into permanent coverage, so the loop you fixed in March doesn't quietly come back in June after a prompt edit.

Frequently asked questions

How do I stop my Claude agent from calling the same tool repeatedly?

Make tool results explain themselves — include a reason field on empty or error results so the model knows whether retrying could help. Then enforce a hard iteration cap and a dedupe guard that intercepts byte-identical repeat calls and returns the prior result instead of re-executing.

Why does Claude invent arguments like fake IDs?

Usually because a required schema field is missing from the conversation and the model would rather guess than violate the schema. Make the field optional where possible, add a system rule forbidding invented identifiers, and validate inputs in the tool so bad values fail loudly and recoverably.

What's the fastest way to find the root cause of an agent failure?

Log and replay the full message array for the failing run — system prompt, tool definitions, and every tool call and result. Read it in order to find the turn where the model's decision first diverged. The visible symptom is usually several turns downstream of the actual cause.

Should I debug with Opus, Sonnet, or Haiku?

Reproduce the bug on whatever model runs in production. A loop that appears on Haiku 4.5 may vanish on Opus 4.8 simply because the larger model reads the tool result more carefully — which masks the underlying design flaw instead of fixing it. Fix the tool and prompt first, then choose the model on cost and latency.

Bringing agentic AI to your phone lines

CallSphere takes these same debugging disciplines — transcript replay, dedupe guards, and self-describing tool results — and applies them to voice and chat agents that answer every call, 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.