Debugging Claude Code Agents: Loops, Bad Tool Calls (Cybersecurity Threat Detection Claude Code)

The first time a Claude Code agent gets stuck investigating a security alert, it rarely fails loudly. It quietly re-runs the same enrichment query four times, each time with a slightly different argument, burning tokens while the analyst waits. On a threat-detection platform — where the agent's job is to triage alerts, pull context from a SIEM, and decide whether something is a real incident — these silent failure modes are the difference between a tool engineers trust and one they mute. This post is about debugging those failures directly: how to see them, why they happen, and what to change.

I'll keep the examples grounded in a realistic build: an agent that receives a raw detection (say, an unusual login), calls tools to enrich it (geo lookup, asset inventory, recent auth history), and produces a verdict with a confidence score. That shape exposes every classic agentic bug.

The three failure modes you will actually hit

Agentic debugging is not like debugging a function. The model is non-deterministic, the control flow is decided at runtime, and the bug is usually a reasoning bug dressed up as a tool error. In practice, threat-detection agents fail in three recognizable ways. The first is looping: the agent calls a tool, gets a result it doesn't know how to use, and calls the same tool again hoping for a better answer. The second is wrong tool selection: faced with twelve MCP tools, it reaches for query_firewall_logs when the alert is clearly an endpoint event. The third is hallucinated arguments: it invents an asset ID, a timestamp format, or a CIDR range that no tool actually accepts.

What makes these hard is that each one can masquerade as the others. A hallucinated argument causes a tool error, the agent retries, and now you have a loop. So the first rule of debugging Claude Code agents is: capture the full trace before you theorize. Every prompt, every tool call with its exact arguments, every tool result, and the model's text between them. Without the intermediate reasoning, you are guessing.

Make the agent observable before you make it smarter

Most teams try to fix loops by adding more instructions to the system prompt. That is backwards. Start by instrumenting. Claude Code exposes hooks that fire around tool use; wire a PreToolUse and PostToolUse hook to log a structured record — tool name, arguments, latency, result size, and a hash of the result. Within minutes you can answer the only question that matters: what is the agent actually doing?

The single most valuable signal is a repeated (tool name, argument-hash) pair within one run. That is the fingerprint of a loop. The second most valuable is a tool result that the agent received but whose content never appears in its subsequent reasoning — a sign it didn't understand the result and is about to retry or hallucinate.

flowchart TD
  A["Detection alert in"] --> B["Agent picks a tool"]
  B --> C{"Tool call valid?"}
  C -->|No: bad args| D["Hallucinated-arg failure"]
  C -->|Yes| E["Tool returns result"]
  E --> F{"Result used in next step?"}
  F -->|No, same call repeats| G["Loop detected"]
  F -->|Yes| H["Verdict produced"]
  D --> I["Log trace & classify failure"]
  G --> I
  I --> B

Killing loops with structure, not pleading

Once a loop is visible, you fix it at the harness level, not by writing "please do not repeat yourself" in the prompt. The most reliable cure is a deterministic loop breaker in your PreToolUse hook: if the same tool is about to be called with the same arguments a third time in one run, deny the call and inject a short observation — "You already ran this query twice with the same arguments and got the same result; either change the arguments or proceed with what you have." That single message resolves a large share of loops because it gives the model new information instead of a wall.

The deeper fix is to ask why the model thought repeating would help. Usually the tool returned an empty or ambiguous result and the agent assumed it was transient. Returning richer, self-describing tool results — "0 rows matched; the asset_id 'srv-4412' was not found in inventory" instead of just [] — removes the ambiguity that drives the loop. Threat-detection tools that return explicit "not found" reasons cut retry loops dramatically.

Fixing wrong tool calls: shrink the menu, sharpen the names

Wrong tool selection is almost always a tools-definition problem, not a model problem. When an agent has fifteen overlapping MCP tools with vague descriptions, even Opus 4.8 will occasionally pick the wrong one. The fixes are unglamorous and effective. Name tools by intent (get_login_history_for_user, not auth_query). Write descriptions that say when not to use the tool. And cut the surface area: an agent with six well-scoped tools chooses correctly far more often than one with twenty.

For a triage agent, I also gate tools by phase. During enrichment, only enrichment tools are exposed; the containment tools (isolate host, disable account) only appear once a verdict crosses a threshold. Fewer choices at each step means fewer wrong choices, and it doubles as a safety boundary.

Hallucinated arguments: validate at the boundary

You cannot prompt a model into never inventing an argument, so you catch it deterministically. Every tool should validate its inputs against a strict schema and return a precise, actionable error — not a stack trace. If the agent passes a malformed timestamp, the tool replies, "Expected ISO-8601 like 2026-06-06T14:00:00Z; received 'last Tuesday'." That correction loop, where the tool teaches the model the right shape, fixes hallucinated args far more reliably than any system-prompt rule.

A subtle source of hallucinated arguments is the agent reaching for data it was never given. If it needs an asset ID it never saw, it will fabricate a plausible one. The fix is to make the missing data explicit: provide an inventory_lookup tool and instruct the agent to resolve identifiers through it rather than guessing. Hallucination thrives in the gaps between what the agent knows and what it needs; close those gaps with tools.

Reproducing the bug when the model is non-deterministic

The frustration of agentic debugging is reproduction. The agent looped once and you can't make it happen again. Build a small replay harness: feed the exact recorded alert back through the agent, pin the model version, and run it five times. If the loop reappears even once, you have a reproducer to test fixes against. If it never reappears, treat it as a tail-risk you mitigate with a loop breaker rather than chase forever. Logging every run's full transcript — keyed by alert ID — is what makes this possible at all.

Frequently asked questions

Why does my Claude Code agent keep calling the same tool repeatedly?

Almost always because the tool returned an ambiguous or empty result the agent couldn't interpret, so it retried hoping for a better one. Make tool results self-describing (explain why zero rows matched) and add a deterministic loop breaker in a PreToolUse hook that blocks the third identical call and injects a corrective note.

How do I stop the agent from inventing tool arguments?

Validate inputs at the tool boundary with a strict schema and return a precise error that shows the expected format. Also give the agent a lookup tool for any identifier it would otherwise guess. Hallucinated arguments are a symptom of missing data plus loose validation, not of the model being careless.

What is the fastest way to debug an agentic threat-detection workflow?

Instrument before you reason. Use Claude Code's PreToolUse and PostToolUse hooks to log every tool call, its arguments, and its result, then look for repeated argument hashes and results that never influence later reasoning. Those two signals expose loops and misunderstanding faster than reading prompts.

Should I fix agent bugs in the prompt or the harness?

Fix structural failures (loops, retries, dangerous calls) deterministically in the harness via hooks and validation; reserve the prompt for genuine reasoning guidance. Harness fixes are testable and reliable, whereas prompt pleading degrades as the model and context change.

Bringing agentic AI to your phone lines

The same observability-first debugging discipline powers CallSphere, where multi-agent voice and chat assistants answer every call, call tools mid-conversation, and book work around the clock without looping or guessing. 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.