Claude Code: Debugging Multi-Agent Teams

· 4 min read

Overview

Debugging a single AI agent is straightforward. Debugging a team of agents that communicate, delegate, and depend on each other is a different challenge entirely. When an agent team produces unexpected output, the root cause could be in the orchestrator logic, in an individual agent's prompt, in the handoff between agents, or in the tool calls that agents make along the way.

Claude Code provides several built-in capabilities that make debugging multi-agent systems more tractable. This guide covers systematic techniques for identifying, isolating, and fixing problems in multi-agent workflows built with the Claude Agent SDK.

When to Use These Techniques

Not every problem requires full-stack agent debugging. Use these techniques when:

Systematic Debugging with Claude Agent SDK

1. Enable Verbose Logging on the Orchestrator

The first step in any debugging session is gaining visibility into what each agent is doing. The Claude Agent SDK supports structured logging that captures every message exchange.

import { Agent, Orchestrator } from "@anthropic-ai/agent-sdk";

const orchestrator = new Orchestrator({
  agents: [researchAgent, writerAgent, reviewerAgent],
  logging: {
    level: "debug",
    includeToolCalls: true,
    includeHandoffs: true,
    format: "structured",
  },
});

const result = await orchestrator.run(taskInput);

// Access the full execution trace
for (const step of result.trace) {
  console.log(`[${step.agent}] ${step.type}: ${JSON.stringify(step.data)}`);
}

This trace gives you a chronological view of every decision the orchestrator made, every message passed between agents, and every tool call with its parameters and response.

2. Isolate Individual Agents

When you suspect a specific agent is causing problems, run it in isolation with the exact input it would receive from the upstream agent.

import { Agent } from "@anthropic-ai/agent-sdk";

const writerAgent = new Agent({
  name: "writer",
  model: "claude-sonnet-4-20250514",
  instructions: "Write a blog post based on the provided research notes.",
  tools: [formatTool, citationTool],
});

// Capture the handoff payload from a previous run's trace
const isolatedInput = {
  researchNotes: "...", // paste from trace
  targetLength: 800,
  tone: "professional",
};

const result = await writerAgent.run(JSON.stringify(isolatedInput));
console.log(result.output);

This technique quickly reveals whether the problem is in the agent itself or in the data it receives.

3. Validate Handoff Schemas

A common source of bugs in multi-agent systems is schema mismatch between agents. One agent produces output in a format that the next agent does not expect.

import { z } from "zod";
import { Agent } from "@anthropic-ai/agent-sdk";

const ResearchOutputSchema = z.object({
  findings: z.array(z.object({
    topic: z.string(),
    summary: z.string(),
    sources: z.array(z.string()),
    confidence: z.number().min(0).max(1),
  })),
  metadata: z.object({
    queryCount: z.number(),
    timeSpentMs: z.number(),
  }),
});

// Add validation at the handoff point
function validateHandoff<T>(data: unknown, schema: z.ZodSchema<T>, fromAgent: string, toAgent: string): T {
  const result = schema.safeParse(data);
  if (!result.success) {
    console.error(`Handoff validation failed: ${fromAgent} -> ${toAgent}`);
    console.error(`Errors: ${JSON.stringify(result.error.issues, null, 2)}`);
    throw new Error(`Schema mismatch in handoff from ${fromAgent} to ${toAgent}`);
  }
  return result.data;
}

4. Add Checkpoint Assertions

Insert assertions between agent steps to catch problems early rather than letting them propagate through the entire team.

import { Orchestrator } from "@anthropic-ai/agent-sdk";

const orchestrator = new Orchestrator({
  agents: [researchAgent, writerAgent, reviewerAgent],
  hooks: {
    afterAgentRun: async (agentName, output) => {
      if (agentName === "researcher") {
        const parsed = JSON.parse(output);
        if (!parsed.findings || parsed.findings.length === 0) {
          throw new Error("Research agent returned no findings — aborting pipeline");
        }
        if (parsed.findings.some((f: any) => f.confidence < 0.3)) {
          console.warn("Low-confidence findings detected — review may flag quality issues");
        }
      }
    },
  },
});

5. Trace Tool Call Failures

Tool calls are a frequent source of agent errors. Agents may hallucinate tool parameters, misinterpret tool responses, or retry failed calls in unproductive loops.

import { Tool } from "@anthropic-ai/agent-sdk";

function wrapToolWithDebugging(tool: Tool): Tool {
  const originalExecute = tool.execute.bind(tool);
  return {
    ...tool,
    execute: async (params: Record<string, unknown>) => {
      console.log(`[TOOL:${tool.name}] Called with:`, JSON.stringify(params));
      const startTime = Date.now();
      try {
        const result = await originalExecute(params);
        const duration = Date.now() - startTime;
        console.log(`[TOOL:${tool.name}] Returned in ${duration}ms:`, JSON.stringify(result).slice(0, 500));
        return result;
      } catch (error) {
        console.error(`[TOOL:${tool.name}] Failed:`, error);
        throw error;
      }
    },
  };
}

Integration with Agent Teams

These debugging techniques become especially powerful when applied to generated agent teams. When you use Build Agents Store to generate a multi-agent configuration, the resulting team definition includes agent names, roles, and expected handoff patterns. You can use this structure to automatically generate validation schemas and checkpoint assertions.

Common debugging patterns for generated teams:

The key insight is that debugging multi-agent systems requires treating the team as a distributed system. The same principles that apply to debugging microservices — structured logging, schema validation, circuit breakers, and isolated testing — apply to multi-agent AI teams.

Skip the setup — generate agent teams instantly →