sub-agent-delegation

name

sub-agent-delegation

description

Delegate work to sub-agents via the Task/Agent tool — parallel research, isolated context windows, specialized expertise. Covers when sub-agents help vs hurt, prompt shape, and result handling. Use this skill when building agents that need to research in parallel, process independent work items, or isolate context-heavy sub-tasks. Activate when: sub-agents, Task tool, Agent tool, parallel agents, agent delegation, spawn agent, multi-agent.

Sub-Agent Delegation

Sub-agents are isolated agent invocations spawned from a parent. They run in a fresh context, do focused work, and return a single result. Use them to parallelize, specialize, and protect context.

When to Use

• Parallel research (5 topics × 1 agent each, run concurrently)
• Context-heavy sub-tasks that would bloat the parent's window
• Specialist work (a "code reviewer" sub-agent, a "security auditor" sub-agent)
• Independent work items where errors shouldn't compound

When NOT to Use

• Trivial single-step tasks — sub-agent overhead isn't worth it
• Tasks that need to see the parent's full context — you'll have to pass it, defeating the isolation
• Highly interactive work — sub-agents don't ask clarifying questions back

The Model

Parent agent sees a Task (or Agent) tool. When invoked, it spawns a sub-agent with:

• A fresh conversation (no parent history)
• Its own tool set (may be a subset of parent's)
• A single prompt describing the work
• A requirement to return ONE final response

The sub-agent's intermediate tool calls are NOT visible to the parent — only the final result.

SDK Usage

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

for await (const msg of query({
  prompt: "Research and compare the top 3 vector databases. Summarize pros/cons.",
  options: {
    model: "claude-sonnet-4-6",
    allowedTools: ["Task", "WebFetch", "Read"],
  },
})) { /* ... */ }

The parent agent decides to invoke Task with a prompt like "Research Pinecone's pros and cons. Use WebFetch. Report in under 200 words." Three parallel Task calls, three independent contexts, three summaries returned.

Parallel Spawning

The parent can issue multiple tool calls in one step:

Parent: Let me research all three in parallel.
[tool_use: Task]   Research Pinecone...
[tool_use: Task]   Research Weaviate...
[tool_use: Task]   Research Qdrant...

Three sub-agents run concurrently. Parent waits for all three results, then synthesizes.

Prompt Shape for Sub-Agents

Sub-agents start with nothing. Prompt them like a smart colleague who just walked in:

1. State the goal
2. Give enough context to make judgment calls
3. Specify output format and length
4. List what's in scope vs out of scope

Bad prompt: "Find info about Pinecone" Good prompt: "Research Pinecone as a vector DB for a 10M-vector production workload. Find pricing, latency at 10M scale, clustering support, filtering support. Report as a 200-word summary with bullet points for each metric. Do not cover marketing fluff."

Handling Sub-Agent Results

hooks: {
  PostToolUse: async ({ toolName, output }) => {
    if (toolName === "Task") {
      await db.logDelegation({ prompt: output.prompt, result: output.result });
    }
  },
}

Log every delegation. Sub-agents can fail, loop, or hallucinate like any agent — audit them.

Context Economics

Sub-agents cost extra — they run a full model inference. But they save parent tokens:

• Without sub-agent: parent processes full raw research → 50K tokens in context
• With sub-agent: parent sees 500-word summary → 500 tokens in context

Net: you pay more compute, save context window for reasoning. Worth it when parent is doing long reasoning on top of research.

Specialization Pattern

Create multiple agent "personas" — each a sub-agent type with specialized prompts and tools:

// Parent system prompt:
"You orchestrate work across:
- 'researcher' sub-agents (WebFetch, Read) for gathering info
- 'reviewer' sub-agents (Read, Grep) for code review
- 'writer' sub-agents (Read, Write) for drafting

Delegate to the right one based on the task."

The parent plans; specialists execute.

Sub-Agent Error Handling

A sub-agent can return errors:

• "I couldn't find relevant info" — retry with refined prompt or move on
• "The tool I need isn't available" — parent decides alternative
• Silent bad output — parent should verify critical results before using

Always have the parent sanity-check sub-agent output before passing to user.

Limits

• No nested delegation by default — sub-agents don't spawn sub-sub-agents (prevents runaway chains)
• Sub-agent budget tokens come from parent's budget
• Too many parallel sub-agents can rate-limit you
• Sub-agents can't communicate with each other — only through the parent

Anti-Patterns

1. Delegating trivial work — sub-agent spin-up costs more than the task
2. Needing the parent's context — passing full history to sub-agent defeats purpose
3. Sub-agent that returns "the full details" — parent gets a firehose; should return a summary
4. No log of delegations — can't debug when something went wrong
5. Unbounded parallel spawn — 50 sub-agents = 50× rate limit consumption

Best Practices

1. Use sub-agents for research, context isolation, specialist work
2. Prompt them self-contained — they have no prior context
3. Require summarized output (word limit)
4. Parallelize independent work; sequentialize when dependencies exist
5. Log every delegation with prompt and result
6. Cap parallel spawn count (e.g., max 5 concurrent)
7. Have parent verify critical sub-agent output before acting on it