multi-agent-orchestration

name	multi-agent-orchestration
description	Coordinate multiple AI agents for complex tasks — decomposition, delegation, and synthesis
lastReviewed	2026-04-30T00:00:00.000Z

Multi-Agent Orchestration Skill

Decompose complex problems into agent-appropriate subtasks, delegate effectively, and synthesize results.

⚠️ Rapid Evolution Domain

Multi-agent patterns are evolving rapidly. This skill captures stable patterns while acknowledging the field is in flux.

Refresh triggers:

• New orchestration frameworks (LangGraph, AutoGen, CrewAI releases)
• Claude/GPT native multi-agent features
• VS Code Copilot agent architecture changes

Last validated: February 2026

---

Core Concepts

When to Use Multi-Agent

Scenario	Single Agent	Multi-Agent
Simple code edit	✅	❌ Overkill
Multi-file refactor	✅ (if capable model)	⚠️ Consider
Research + implement	⚠️ Long context	✅ Decompose
Cross-domain task	❌ Context overload	✅ Specialists
Parallel independent work	❌ Sequential	✅ Parallel agents

Agent Roles

Role	Responsibility	Example
Orchestrator	Decompose, delegate, synthesize	Main chat session
Specialist	Deep expertise in one domain	Security reviewer agent
Worker	Execute well-defined subtask	"Find all usages of X"
Critic	Validate, review, improve	Code review agent

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Decomposition Patterns

1. Horizontal Decomposition (Parallel)

Split task into independent subtasks that can run simultaneously.

┌─────────────────┐
│  Orchestrator   │
└───────┬─────────┘
        │ decompose
   ┌────┴────┬────────┐
   ▼         ▼        ▼
┌─────┐  ┌─────┐  ┌─────┐
│ A1  │  │ A2  │  │ A3  │   (parallel)
└──┬──┘  └──┬──┘  └──┬──┘
   └────────┼────────┘
            ▼
      synthesize

When to use:

• Tasks have no dependencies
• Results can be merged mechanically
• Time is critical

Example: "Search for security issues in auth, api, and database modules"

2. Vertical Decomposition (Pipeline)

Chain agents where each builds on previous output.

┌─────────────────┐
│  Orchestrator   │
└───────┬─────────┘
        ▼
    ┌───────┐
    │  A1   │ → research
    └───┬───┘
        ▼
    ┌───────┐
    │  A2   │ → analyze
    └───┬───┘
        ▼
    ┌───────┐
    │  A3   │ → implement
    └───────┘

When to use:

• Each step needs output from previous
• Context builds incrementally
• Quality gates between steps

Example: "Research best practices → Design API → Implement → Review"

3. Hierarchical Decomposition (Tree)

Orchestrator delegates to sub-orchestrators who manage workers.

┌─────────────────┐
│   Root Orch     │
└───────┬─────────┘
   ┌────┴────┐
   ▼         ▼
┌─────┐   ┌─────┐
│SubO1│   │SubO2│    (sub-orchestrators)
└──┬──┘   └──┬──┘
 ┌─┴─┐    ┌──┴──┐
 ▼   ▼    ▼     ▼
┌─┐ ┌─┐  ┌─┐   ┌─┐
│W│ │W│  │W│   │W│   (workers)
└─┘ └─┘  └─┘   └─┘

When to use:

• Very complex tasks
• Different domains within task
• Scale beyond single orchestrator's context

---

Delegation Best Practices

Crafting Agent Instructions

When delegating to a subagent, specify:

Element	Purpose	Example
Context	What they need to know	"Working on the AI assistant VS Code extension"
Scope	Clear boundaries	"Only look in /src/services"
Output	Expected format	"Return JSON with findings"
Constraints	What NOT to do	"Don't modify files, only report"

Template for Subagent Prompt

**Task:** [One-sentence objective]

**Context:**
- Project: [name/type]
- Relevant files: [list]
- What's already done: [state]

**Scope:**
- DO: [specific actions]
- DON'T: [boundaries]

**Expected Output:**
[Format and content expectations]

**Success Criteria:**
[How you'll know it's done right]

---

Synthesis Patterns

Merging Agent Outputs

Pattern	When	How
Concatenate	Independent results	Simple append
Deduplicate	Overlapping searches	Hash/compare
Vote	Multiple opinions	Majority wins
Synthesize	Diverse perspectives	LLM summary
Validate	Critical decisions	Critic agent reviews

Conflict Resolution

When agents disagree:

1. Identify conflict type

   • Factual (check sources)
   • Opinion (escalate to user)
   • Interpretation (provide both views)

2. Resolution strategies

   • Ask clarifying questions
   • Request evidence from agents
   • Escalate to more capable model
   • Present options to user

---

VS Code Copilot Patterns

Using runSubagent Effectively

The runSubagent tool enables orchestration within VS Code:

// Good: Clear task with expected output
await runSubagent({
  prompt: `Search the codebase for all error handling patterns.
           Return a JSON array of: {file, line, pattern, quality}`,
  description: "Find error patterns"
user-invokable: false
});

// Bad: Vague delegation
await runSubagent({
  prompt: "Look for problems in the code",  // Too vague
  description: "Find issues"
user-invokable: false
});

When to Use Subagent vs Direct

Scenario	Approach
Simple search	Direct grep_search
Complex multi-step search	runSubagent
Single file edit	Direct replace_string_in_file
Multi-file coordinated change	Consider subagent for planning
Research + implementation	Subagent for research, direct for implementation

---

Common Anti-Patterns

❌ Over-Orchestration

Problem: Using multiple agents for simple tasks Symptom: Slower, more expensive, no quality gain Fix: Trust capable models for multi-step tasks up to complexity threshold

❌ Insufficient Context

Problem: Agents lack needed information Symptom: Repeated clarification requests, wrong assumptions Fix: Front-load context in delegation prompt

❌ No Synthesis Strategy

Problem: Raw agent outputs dumped on user Symptom: User must manually integrate results Fix: Plan synthesis before decomposition

❌ Circular Dependencies

Problem: Agent A needs B's output, B needs A's output Symptom: Deadlock or infinite loops Fix: Identify and break cycles in task graph

---

Framework Landscape (2026)

Framework	Strength	Use Case
LangGraph	State machines, cycles	Complex workflows
AutoGen	Conversation patterns	Research, debate
CrewAI	Role-based teams	Business processes
VS Code Agents	IDE integration	Code tasks
Semantic Kernel	.NET native	Enterprise C#

---

the AI assistant-Specific Patterns

Heir Orchestration

your AI assistant can coordinate heirs for cross-platform tasks:

your AI assistant (orchestrator)
├── VS Code Heir → code analysis
├── M365 Heir → document synthesis
└── Global Knowledge → pattern matching

Skill Selection as Orchestration

When the AI assistant runs Skill Selection Optimization (SSO), it's a form of self-orchestration:

1. Survey available skills (agents)
2. Match to task requirements
3. Load relevant skills
4. Execute with combined expertise

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Implementation Checklist

When designing multi-agent workflows:

• Can a single capable model handle this?
• Are subtasks truly independent (or pipelined)?
• Is context sufficient for each agent?
• Is output format clearly specified?
• Is synthesis strategy defined?
• Are failure modes handled?
• Is the orchestration overhead justified?

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Related Skills

• skill-selection-optimization — Pre-task skill loading
• prompt-engineering — Crafting effective agent prompts
• appropriate-reliance — Knowing when to trust agent output
• root-cause-analysis — Debugging multi-agent failures

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Multi-agent orchestration is powerful but not always necessary. Start simple, add agents when complexity demands it.