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BUILDERParallel Sub-Agent Coordination

How Builders Use Sub-Agents for Parallel Research Tasks

Most agentic setups break down when multiple agents work on the same problem at once. This resource shows how to structure parallel sub-agent tasks so each agent has a clear scope, a defined output format, and a handoff the next agent can consume cleanly. Includes the coordination pattern that keeps outputs coherent without constant manual review.

Parallel Sub-Agent Coordination

Three Agents, One Coherent Output

The coordination pattern that prevents parallel sub-agents from producing contradictory, redundant work.

How the Three-Agent Model Works
1

Research Agent

Scopes a specific domain and produces a structured findings file. Does not implement anything.

2

Implementation Agent

Reads the findings file and builds only what falls within its defined scope. Never reads the full conversation history.

3

Review Agent

Reads both outputs and flags contradictions, gaps, or scope drift before you accept either result.

The Handoff Format
findings

Array of observations with source references. The implementation agent reads only this field.

open_questions

Unresolved issues the next agent must address before proceeding.

blockers

Hard stops that require human input before the next stage runs.

scope_boundary

Explicit list of files and directories this agent is allowed to touch.

Common Mistakes

Sharing full context

Passing the entire conversation to each agent bloats context and introduces contradictory instructions from earlier turns.

No scope boundaries

Two agents writing to the same file without coordination creates merge conflicts and inconsistent outputs.

Skipping the review step

Without a review agent, contradictions between research and implementation outputs stay invisible until they cause a real failure.

Key Takeaways

  • 1Define a single scope boundary per agent upfront so research, implementation, and review agents never write to the same files simultaneously.
  • 2Use a structured JSON handoff with findings, open questions, and blockers so each agent consumes the previous output without ambiguity.
  • 3Assign a coordinator or run a dedicated review agent last to flag contradictions between research and implementation outputs before you accept either.
  • 4Cap each sub-agent's context to its own scope. Passing the full conversation history defeats the purpose of parallelism.
  • 5Run research and implementation in separate Claude sessions, not sequentially in one chat, to prevent context bleed between agents.
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