---

name: orchestration-protocol version: 1.0.0 author: jwilger repository: jwilger/claude-code-plugins description: Multi-agent orchestration patterns for delegation, coordination, and conflict resolution tags:

• orchestration
• multi-agent
• coordination
• delegation portability: medium dependencies:
• user-input-protocol

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Orchestration Protocol

Version: 1.0.0 Portability: Medium

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Objective

Defines patterns for orchestrating multiple specialized agents in complex workflows, including delegation strategies, context provision, conflict resolution, and workflow state management.

Purpose: Enable coordinated multi-agent systems where a main orchestrator delegates to specialized agents while maintaining workflow integrity and clear responsibility boundaries.

Scope:

• Included: Delegation patterns, context provision, agent specialization, conflict resolution, workflow coordination
• Excluded: Specific agent implementations, tool-specific orchestration APIs

---

Core Principles

Principle 1: Orchestrator Delegates, Never Acts Directly

The Principle: The main orchestrator coordinates work by delegating to specialized agents but never performs specialized work itself.

Why this matters: Mixing orchestration logic with specialized work creates single points of failure and makes systems harder to maintain. Specialization enables focused expertise.

How to apply:

• Orchestrator decides WHAT to do and WHO should do it
• Specialized agents decide HOW to do their specific work
• Orchestrator never writes code, edits files, or performs domain-specific tasks
• All specialized work flows through appropriate agents

Example:

❌ Bad: Orchestrator writes test directly
Orchestrator: (uses Write tool to create test file)

✓ Good: Orchestrator delegates to test-writing agent
Orchestrator: Delegate to TestWriter agent with context:
              "Create test for user authentication"
TestWriter: (writes test using domain knowledge)

Principle 2: Fresh Context Per Agent (Zero Memory Assumption)

The Principle: Agents have zero context from the main conversation. Every delegation must include complete context.

Why this matters: Agents don't share conversation history. Assuming they know "what we discussed" leads to failures.

What to provide:

• File paths being modified
• Current state (what's passing/failing)
• Requirements (what "done" looks like)
• Domain constraints (types to use, patterns to follow)
• Error messages (exact text, not summaries)

How to apply:

❌ Bad: Vague delegation
"Continue where we left off"
"Fix the issue we discussed"
"You know what to do"

✓ Good: Complete context
"The test at `tests/auth_test.rs:45` fails with error: 'Email type not found'.
Create the Email type in `src/domain/types.rs` using newtype pattern.
Test expects: `Email::new(string) -> Result<Email, ValidationError>`"

Template:

WORKING_DIRECTORY: [Absolute path to project root]
TASK: [What to accomplish]
FILES: [Specific paths]
CURRENT STATE: [What exists, what's failing]
REQUIREMENTS: [Success criteria]
CONSTRAINTS: [Patterns to follow, types to use]
ERROR: [Exact error message if applicable]

Principle 3: Workflow Gates Enforce Discipline

The Principle: Agents require explicit context confirmations before proceeding, preventing workflow step skipping.

Why this matters: Complex workflows have dependencies. Skipping steps (like domain review) leads to incomplete or incorrect work.

Gate pattern:

Agent checks for required context marker.
If missing → Reject with "GATE FAILED: Missing [context]"
If present → Proceed with work

Example gates:

Test-writing agent requires:
- Confirmation of acceptance criteria
- OR confirmation previous cycle completed

Implementation agent requires:
- Confirmation test exists and fails
- Confirmation domain review passed

Domain review agent requires:
- Confirmation of which phase is being reviewed (after test or after implementation)

Benefits:

• Prevents "quick fix" shortcuts
• Forces conscious workflow state tracking
• Makes workflow violations immediately visible
• Documents that required steps occurred

Principle 4: Domain Expert Has Veto Power

The Principle: In multi-agent systems with domain modeling, the domain expert agent has veto authority over design decisions that violate domain principles.

Why this matters: Domain integrity is foundational. Compromising it for expediency creates technical debt that compounds.

Domain veto authority covers:

• Primitive obsession (String where Email type should exist)
• Invalid states representable (type allows impossible situations)
• Parse-don't-validate violations
• Domain boundary violations

Resolution protocol:

1. Domain raises concern
2. Affected agent responds with rationale
3. Orchestrator facilitates discussion
4. Seek compromise
5. If no consensus after 2 rounds → Escalate to user

Example:

TestWriter: Creates test using `String` for email parameter
  ↓
DomainReviewer: VETO - Primitive obsession. Use Email type.
  ↓
TestWriter: "Email type doesn't exist yet, using String for now"
  ↓
DomainReviewer: "That's why I review BEFORE implementation - so I can create Email type"
  ↓
Orchestrator: "DomainReviewer will create Email type, TestWriter will revise test to use it"
  ↓
TestWriter: Revises test with Email type
  ↓
DomainReviewer: Creates Email type
  ↓
Consensus reached, proceed

---

Constraints and Boundaries

DO (Orchestrator):

• Decide which agent handles which task
• Provide complete context to every agent
• Enforce workflow gates
• Facilitate agent conflicts
• Track workflow state
• Monitor for agent pause signals (questions, blockers)
• Ensure required confirmations before proceeding

DON'T (Orchestrator):

• Perform specialized work (write code, edit files)
• Skip workflow steps ("just this once")
• Assume agents have context ("as we discussed")
• Override domain veto without user consultation
• Let agent debates drag (escalate after 2 rounds)
• Bypass gates ("I know this step isn't needed")

DO (Specialized Agents):

• Focus on your specialty
• Validate input gates
• Provide clear output
• Signal when user input needed
• Raise concerns when design violates principles
• Document your decisions

DON'T (Specialized Agents):

• Work outside your specialty
• Assume context from main conversation
• Proceed when gate validation fails
• Silently accept violations of your principles

Rationale: Clear boundaries prevent responsibility diffusion and maintain workflow integrity.

---

Usage Patterns

Pattern 1: Agent Specialization Hierarchy

Scenario: Multi-agent system needs clear responsibility boundaries.

Specialization by concern:

Orchestrator (coordinates)
├─ TestWriter (test files only)
├─ Implementer (production code only)
├─ DomainModeler (type definitions only)
├─ Reviewer (read-only analysis)
└─ ConfigManager (configuration files only)

Delegation rules:

• File pattern match → Appropriate agent
• Never multiple agents editing same file simultaneously
• Each agent has exclusive domain
• Orchestrator never bypasses agents

Example:

User request: "Add user authentication"

Orchestrator analysis:
- Needs: Test (TestWriter) + Types (DomainModeler) + Implementation (Implementer)

Orchestrator workflow:
1. Delegate to TestWriter: "Create failing test for authentication"
2. Wait for TestWriter completion
3. Delegate to DomainModeler: "Review test, create types"
4. Wait for DomainModeler completion
5. Delegate to Implementer: "Implement to pass test"
6. Wait for Implementer completion
7. Delegate to DomainModeler: "Review implementation"
8. Complete when DomainModeler approves

Pattern 2: Workflow State Tracking

Scenario: Complex multi-step workflow needs state management.

State markers:

Workflow phases:
- Test: Not started | In progress | Failing (expected) | Passing | Approved
- Types: Not started | In progress | Created | Approved
- Implementation: Not started | In progress | Test passing | Approved

Current state determines next valid action.

Gate enforcement:

def can_proceed_to_implementation(state):
    return (
        state.test == "Failing (expected)" and
        state.types == "Created" and
        state.domain_review_after_test == "Approved"
    )

if not can_proceed_to_implementation(state):
    return "GATE FAILED: Prerequisites not met"

Pattern 3: Agent Pause and Resume

Scenario: Long-running agent needs user input mid-task.

Pause signal:

{
  "status": "PAUSED",
  "reason": "USER_INPUT_NEEDED",
  "question": "Found 3 validation approaches. Which should I use?",
  "options": ["Strict RFC compliance", "Lenient", "Custom rules"],
  "state_checkpoint": "agent-123-checkpoint-456"
}

Orchestrator response:

1. Detect pause signal
2. Ask user via AskUserQuestion
3. Resume agent with answer and checkpoint reference

Resume:

{
  "status": "RESUMING",
  "checkpoint": "agent-123-checkpoint-456",
  "user_answer": "Strict RFC compliance",
  "instruction": "Continue with user's choice"
}

Pattern 4: Conflict Resolution

Scenario: Two agents disagree on approach.

Debate protocol:

Round 1:
  Agent A: States position
  Agent B: States counter-position
  Orchestrator: Summarizes both positions clearly

Round 2:
  Agent A: Responds to B's points
  Agent B: Responds to A's points
  Orchestrator: Proposes compromise

If consensus:
  Proceed with agreed approach

If no consensus after Round 2:
  Orchestrator: Escalate to user
  User: Makes decision
  Agents: Implement user's decision

Example:

TestWriter: "Test should use String for email (simpler)"
DomainModeler: "Email type required (prevents invalid emails)"
Orchestrator: "TestWriter values simplicity, DomainModeler values safety"

Round 2:
TestWriter: "Email type adds complexity without immediate benefit"
DomainModeler: "Complexity is minimal, benefit is compile-time validation"
Orchestrator: "Compromise: Use Email type now (DomainModeler creates it), keep it simple"

Consensus: Use Email type (DomainModeler wins on principle)

---

Integration with Other Skills

Works well with:

• user-input-protocol: Agents use pause/resume for questions
• tdd-constraints: Orchestrator enforces TDD phase sequence
• domain-modeling: DomainModeler agent applies domain principles
• debugging-protocol: When agents fail, orchestrator applies systematic debugging

Prerequisites:

• Agent framework with delegation capabilities
• Multiple specialized agents
• Context passing mechanism
• Workflow state tracking

---

Common Pitfalls

Pitfall 1: Orchestrator Doing Specialized Work

Problem: "Quick fix" - orchestrator edits file directly

Solution: Always delegate. No exceptions.

Pitfall 2: Vague Context to Agents

Problem: "Fix the bug" without specifying which bug, which file, what error

Solution: Use context template. Be exhaustively specific.

Pitfall 3: Skipping Gates

Problem: "I know domain review isn't needed here"

Solution: Gates exist for discipline. If agent rejects, you skipped something.

Pitfall 4: Letting Debates Drag

Problem: Agents debate for 5+ rounds without resolution

Solution: Escalate after round 2. User breaks tie.

Pitfall 5: Assuming Agent Memory

Problem: "Continue where you left off" to resumed agent

Solution: Provide full context, even on resume. Agents don't remember.

---

Examples

Example 1: Complete TDD Cycle with Orchestration

User: "Add email validation to User model"

Orchestrator:
  State: Starting new feature
  Next: Write failing test

Orchestrator → TestWriter:
  CONTEXT: FIRST_TEST
  TASK: Create failing test for email validation
  FILE: tests/user_test.rs
  REQUIREMENTS:
    - Test User::new() with invalid email
    - Expect validation error
  CONSTRAINTS:
    - Use domain types (Email, not String)
    - One assertion per test

TestWriter: (creates test)
  Result: test_user_rejects_invalid_email FAILS
    Error: "Email type not found"

Orchestrator:
  State: Test written, domain review needed

Orchestrator → DomainModeler:
  CONTEXT: AFTER_RED
  RED_COMPLETE:
    - Test: test_user_rejects_invalid_email
    - Error: "Email type not found"
  TASK: Review test, create Email type
  FILE: src/domain/types.rs

DomainModeler: (reviews test, creates Email type)
  Output: Email type created with validation
  Result: APPROVE - No domain concerns

Orchestrator:
  State: Types created, ready for implementation

Orchestrator → Implementer:
  CONTEXT: IMPLEMENTATION
  RED_COMPLETE:
    - Test: test_user_rejects_invalid_email
    - Error: "unimplemented"
  DOMAIN_REVIEW: PASSED
    - Email type created in src/domain/types.rs
  TASK: Implement User::new() to validate email
  FILES:
    - src/domain/user.rs (implementation)
    - src/domain/types.rs (use Email type)

Implementer: (implements validation)
  Result: test_user_rejects_invalid_email PASSES

Orchestrator:
  State: Implementation complete, domain review needed

Orchestrator → DomainModeler:
  CONTEXT: AFTER_GREEN
  GREEN_COMPLETE:
    - Test: test_user_rejects_invalid_email PASSES
    - Files: src/domain/user.rs, src/domain/types.rs
  TASK: Review implementation for domain integrity

DomainModeler: (reviews implementation)
  Output: APPROVE - Email newtype used correctly, validation at construction

Orchestrator:
  State: Cycle complete, feature implemented
  Report: Email validation added with domain-rich types

Example 2: Agent Conflict Resolution

Orchestrator → TestWriter: "Create test for order total calculation"

TestWriter: (creates test using float for money)

test_order_calculates_total() { order.add_item(10.99, 2); assert_eq!(order.total(), 21.98); }

Orchestrator → DomainModeler: "Review test"

DomainModeler: CONCERN - Primitive obsession
  Money should be Money type, not float
  Floats have precision issues (0.1 + 0.2 != 0.3)

Orchestrator facilitates:
  TestWriter: Why float?
  DomainModeler: Why Money type?

Round 1:
TestWriter: "Float is simpler, everyone understands money as numbers"
DomainModeler: "Money type prevents precision errors and enforces currency"

Orchestrator: "TestWriter values simplicity, DomainModeler values correctness"

Round 2:
TestWriter: "Can we use Decimal instead of custom Money type?"
DomainModeler: "Acceptable compromise - Decimal fixes precision, maintains simplicity"

Consensus: Use Decimal type (compromise)

Orchestrator → TestWriter: "Revise test with Decimal"
TestWriter: (revises)

Orchestrator → DomainModeler: "Re-review"
DomainModeler: APPROVE

Example 3: Agent Pause for User Input

Orchestrator → Implementer: "Implement payment processing"

Implementer: (starts work, encounters decision point)

Implementer output:
AWAITING_USER_INPUT
{
  "context": "Payment processing needs retry strategy",
  "checkpoint": "impl-payment-checkpoint-1",
  "questions": [{
    "question": "How should we handle payment failures?",
    "options": [
      "Immediate failure (no retries)",
      "Retry 3 times with exponential backoff",
      "Retry indefinitely until success"
    ]
  }]
}

Orchestrator: (detects pause)
  1. Asks user via AskUserQuestion
  2. User chooses: "Retry 3 times with exponential backoff"

Orchestrator → Implementer: (resume)
  USER_INPUT_RESPONSE: "Retry 3 times with exponential backoff"
  CHECKPOINT: "impl-payment-checkpoint-1"
  INSTRUCTION: "Continue implementation with user's retry strategy"

Implementer: (resumes with context, completes work)
  Result: Payment processing implemented with 3-retry strategy

---

Verification Checklist

• Orchestrator never performs specialized work
• Every agent delegation includes complete context
• Workflow gates enforced (agents check confirmations)
• Domain expert veto authority respected
• Agent conflicts resolved within 2 rounds or escalated
• Agent pause signals detected and handled
• Workflow state tracked and visible
• Fresh context provided on every agent invocation
• No assumptions about agent memory

---

References

Source Documentation:

• sdlc plugin: commands/shared/orchestration.md

Related Skills:

• user-input-protocol - Agent pause/resume pattern
• tdd-constraints - Workflow orchestrator enforces
• domain-modeling - Domain expert role in orchestration

External Resources:

• Multi-Agent Systems by Wooldridge
• Microservices Patterns by Richardson (orchestration vs choreography)

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Version History

v1.0.0 (2026-02-04)

• Initial extraction from sdlc plugin
• Delegation patterns
• Context provision
• Workflow gates
• Conflict resolution
• Agent specialization
• Medium portability (requires agent delegation framework)

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Metadata

Extraction Source: sdlc/commands/shared/orchestration.md Extraction Date: 2026-02-04 Last Updated: 2026-02-04 Compatibility: Medium portability (requires multi-agent framework) License: MIT