Conductor agent pattern that decomposes complex tasks, dispatches specialist sub-agents, manages dependencies, and synthesizes results into a unified deliverable. Use when: 'coordinate multiple specialist agents', 'orchestrate a complex multi-phase task', 'run a conductor pattern across agents', 'this task needs live agent coordination', 'decompose and dispatch to sub-agents'.
Purpose: Act as a conductor agent that decomposes complex objectives into subtasks, dispatches specialist sub-agents with appropriate isolation and model routing, manages inter-agent dependencies, resolves conflicts between agent outputs, and synthesizes results into a coherent deliverable.
Verify combined result -- run build/test gates on the integrated output
Produce orchestration report -- document what was done, by whom, what conflicts arose, and what the final state is
Surface lessons learned -- what worked, what failed, what to do differently next time
IV. Agent Reference
Agent
Purpose
Model
Best For
scout
Codebase exploration
sonnet
Finding patterns, mapping structure
oracle
External research
opus
Web/docs, best practices
architect
Feature planning
opus
Design, specification
kraken
TDD implementation
opus
Building features
arbiter
Validation/testing
opus
Unit/integration tests
critic
Code review
sonnet
Quality assessment
herald
Release preparation
sonnet
Deployment, changelog
phoenix
Refactor planning
opus
Technical debt
If available_agents is not specified, select from the full roster based on the task.
V. Output
Orchestration Report containing: task decomposition table, pattern selected with rationale, execution log per phase, agent output summaries, conflict resolutions, integrated deliverables list, validation status, and lessons learned
Saved to: project's orchestration output directory or returned inline for lightweight tasks
Format: structured markdown with tables for subtask tracking
VI. Examples
Scenario 1: "Wire the new authentication service across 3 microservices" --> Hierarchical pattern; architect produces integration plan; 3 parallel kraken agents (one per service, worktree isolation); arbiter validates cross-service contract tests; maestro resolves one shared-schema conflict between services A and C.
Scenario 2: "Research MCP architecture patterns and produce an ADR" --> Swarm pattern; scout analyzes internal codebase patterns, oracle researches external MCP ecosystem, second scout catalogs existing ADRs; maestro synthesizes into a single ADR with 3 options ranked by trade-offs.
Scenario 3: "Refactor the event system -- this is high-stakes, get multiple reviews" --> Generator-Critic into Jury; architect proposes refactor plan, critic_1 reviews for correctness, critic_2 reviews for performance, critic_3 reviews for backward compatibility; majority vote selects approach B; kraken implements; arbiter validates with regression suite.
VII. Edge Cases
No specialist agents available: maestro falls back to single-thread execution, performing each subtask sequentially in its own context
Two agents modify the same file: do NOT dispatch both in parallel; run one foreground, apply its changes, then dispatch the second with the updated file state
Agent reports "complete" but verification fails: re-dispatch with the failure output included in the prompt; cap retries at 2 before escalating to the user
Objective is too vague to decompose: ask the user for clarification before dispatching; do not guess at subtask boundaries
VIII. Anti-Patterns
Dispatching agents before completing decomposition -- leads to redundant work and conflicts
Using Opus for mechanical fix agents that have exact instructions -- waste of reasoning budget; route to Sonnet
Launching more than 4 concurrent agents -- diminishing returns from coordination overhead; 3 is the sweet spot
Passing synthesis responsibility to a sub-agent -- the conductor (maestro) must own synthesis; sub-agents produce parts, maestro produces the whole
Skipping the verification gate after integration -- combined outputs can have conflicts that individual agent verification missed