conductor-orchestrator

Conductor Orchestrator — Parallel Multi-Agent Coordinator (v3)

The master coordinator that runs the Evaluate-Loop for any track. Version 3 adds goal-driven entry, parallel execution via worker agents, Board of Directors deliberation, and message bus coordination.

---

Goal-Driven Entry (/go)

The simplest entry point. User states their goal, the system handles everything.

Usage

/go Add Stripe payment integration
/go Fix the login bug
/go Build an admin dashboard

Goal Processing Flow

async function processGoal(userGoal: string) {
  // 1. GOAL ANALYSIS
  const analysis = await analyzeGoal(userGoal);
  /*
    Returns:
    - intent: "feature" | "bugfix" | "refactor" | "research"
    - keywords: ["stripe", "payment", "checkout"]
    - complexity: "minor" | "moderate" | "major"
    - technical: boolean
  */

  // 2. CHECK EXISTING TRACKS
  const existingTrack = await findMatchingTrack(analysis.keywords);

  if (existingTrack) {
    // Resume existing track
    console.log(`Found existing track: ${existingTrack.id}`);
    return resumeOrchestration(existingTrack.id);
  }

  // 3. CREATE NEW TRACK
  const trackId = await createTrackFromGoal(userGoal, analysis);
  /*
    Creates:
    - conductor/tracks/{trackId}/
    - conductor/tracks/{trackId}/spec.md (generated from goal)
    - conductor/tracks/{trackId}/metadata.json (v3)
  */

  // 4. RUN FULL LOOP
  return runOrchestrationLoop(trackId);
}

Goal Analysis

async function analyzeGoal(goal: string) {
  // Use context-explorer to understand codebase
  const codebaseContext = await Task({
    subagent_type: "Explore",
    description: "Understand codebase for goal",
    prompt: `Analyze codebase to understand context for: "${goal}"

      Return:
      1. Related files/components
      2. Existing patterns to follow
      3. Dependencies needed
      4. Potential conflicts with existing code`
  });

  // Classify goal
  const intent = classifyIntent(goal);
  const keywords = extractKeywords(goal);
  const complexity = estimateComplexity(goal, codebaseContext);
  const technical = isTechnicalGoal(goal);

  return { intent, keywords, complexity, technical, codebaseContext };
}

function classifyIntent(goal: string): string {
  const lowerGoal = goal.toLowerCase();

  if (lowerGoal.match(/fix|bug|error|broken|crash|issue/)) return "bugfix";
  if (lowerGoal.match(/refactor|clean|optimize|improve|simplify/)) return "refactor";
  if (lowerGoal.match(/research|investigate|analyze|understand/)) return "research";
  return "feature";
}

Track Matching

async function findMatchingTrack(keywords: string[]): Track | null {
  const tracks = await readTracksFile();

  // Check in-progress tracks first
  const inProgress = tracks.filter(t =>
    t.status === 'IN_PROGRESS' || t.status === 'in_progress'
  );

  for (const track of inProgress) {
    const trackKeywords = extractKeywords(track.name + ' ' + track.description);
    const overlap = keywords.filter(k => trackKeywords.includes(k));

    if (overlap.length >= 2) {
      return track; // Good match
    }
  }

  // Check planned tracks
  const planned = tracks.filter(t =>
    t.status === 'NOT_STARTED' || t.status === 'planned'
  );

  for (const track of planned) {
    const trackKeywords = extractKeywords(track.name + ' ' + track.description);
    const overlap = keywords.filter(k => trackKeywords.includes(k));

    if (overlap.length >= 2) {
      return track;
    }
  }

  return null; // No match, create new track
}

Spec Generation from Goal

async function generateSpecFromGoal(goal: string, analysis: GoalAnalysis): string {
  const spec = await Task({
    subagent_type: "Plan",
    description: "Generate spec from goal",
    prompt: `Generate a specification document for this goal:

      GOAL: "${goal}"

      CODEBASE CONTEXT:
      ${analysis.codebaseContext}

      Create spec.md with:
      1. Overview - what we're building/fixing
      2. Requirements - specific deliverables
      3. Acceptance Criteria - how to verify it works
      4. Dependencies - what this needs
      5. Out of Scope - what we're NOT doing

      Be specific and actionable. Use the codebase context to identify:
      - Existing patterns to follow
      - Files that will be modified
      - Tests that need to pass

      Format as markdown.`
  });

  return spec.output;
}

Escalation During Goal Processing

// If goal is ambiguous, ask for clarification
if (analysis.ambiguous) {
  return askUserQuestion({
    questions: [{
      question: "I need clarification on your goal. Which do you mean?",
      header: "Clarify",
      options: analysis.interpretations.map(i => ({
        label: i.summary,
        description: i.detail
      })),
      multiSelect: false
    }]
  });
}

// If multiple tracks match, ask which one
if (matchingTracks.length > 1) {
  return askUserQuestion({
    questions: [{
      question: "This goal matches multiple existing tracks. Which one?",
      header: "Track",
      options: matchingTracks.map(t => ({
        label: t.name,
        description: `Status: ${t.status}`
      })),
      multiSelect: false
    }]
  });
}

---

Key Changes in v3

From v2

1. Metadata-based state detection — Reads loop_state.current_step from metadata.json
2. Lead Engineer consultation — Consults specialized leads for decisions
3. Resumption support — Exact state recovery if interrupted
4. Explicit checkpoints — Each step writes state to metadata.json
5. Learning Layer — Knowledge Manager + Retrospective Agent

New in v3

6. Parallel Execution — Multiple workers execute DAG tasks simultaneously
7. Board of Directors — 5-member expert deliberation at checkpoints
8. Message Bus — Inter-agent coordination via file-based queue
9. Worker Pool — Dynamic worker creation/cleanup via agent-factory
10. DAG-Aware Planning — Plans include explicit dependency graphs
11. Failure Isolation — One worker failure doesn't block independent tasks

---

State Detection (New v2 Protocol)

Primary: Read metadata.json

async function detectCurrentStep(trackId: string) {
  const metadataPath = `conductor/tracks/${trackId}/metadata.json`;
  const metadata = await readJSON(metadataPath);

  // Migrate v1 to v2 if needed
  if (!metadata.version || metadata.version < 2) {
    metadata = await migrateToV2(trackId, metadata);
    await writeJSON(metadataPath, metadata);
  }

  const { current_step, step_status } = metadata.loop_state;

  return { current_step, step_status, metadata };
}

State Machine Logic (v3)

Current Step	Step Status	Next Action
PLAN	NOT_STARTED	Dispatch loop-planner (with DAG generation)
PLAN	IN_PROGRESS	Resume loop-planner
PLAN	PASSED	Advance to EVALUATE_PLAN
EVALUATE_PLAN	NOT_STARTED	Dispatch loop-plan-evaluator + DAG validation
EVALUATE_PLAN	BOARD_REVIEW	NEW: Invoke Board of Directors if major track
EVALUATE_PLAN	PASSED	Advance to PARALLEL_EXECUTE
EVALUATE_PLAN	FAILED	Go back to PLAN with board conditions
PARALLEL_EXECUTE	NOT_STARTED	NEW: Initialize message bus, dispatch parallel workers
PARALLEL_EXECUTE	IN_PROGRESS	Monitor workers via message bus
PARALLEL_EXECUTE	PASSED	Advance to EVALUATE_EXECUTION
PARALLEL_EXECUTE	PARTIAL_FAIL	Handle failures, continue independent tasks
EVALUATE_EXECUTION	NOT_STARTED	Dispatch evaluators + quick board review
EVALUATE_EXECUTION	PASSED	Check business_sync_required → BUSINESS_SYNC or COMPLETE
EVALUATE_EXECUTION	FAILED	Advance to FIX
FIX	NOT_STARTED	Check fix_cycle_count → dispatch loop-fixer or escalate
FIX	IN_PROGRESS	Resume loop-fixer
FIX	PASSED	Go back to EVALUATE_EXECUTION
BUSINESS_SYNC	NOT_STARTED	Dispatch business-docs-sync
BUSINESS_SYNC	PASSED	Advance to COMPLETE
COMPLETE	—	Run retrospective, cleanup workers, report success
Any	BLOCKED	Check blockers, escalate to user
Any	ESCALATE	Board or lead escalated → user intervention

---

Lead Engineer Consultation System

When to Consult Leads

Before escalating a decision to user, consult the appropriate Lead Engineer:

Question Category	Lead to Consult	Skill Path
Architecture, patterns, component organization	Architecture Lead	.claude/skills/leads/architecture-lead/SKILL.md
Scope interpretation, requirements, copy	Product Lead	.claude/skills/leads/product-lead/SKILL.md
Implementation, dependencies, tooling	Tech Lead	.claude/skills/leads/tech-lead/SKILL.md
Testing, coverage, quality gates	QA Lead	.claude/skills/leads/qa-lead/SKILL.md

Consultation Flow

async function handleDecision(question: Question) {
  // 1. Check Authority Matrix
  const authority = lookupAuthority(question.category);

  // 2. USER_ONLY decisions go straight to user
  if (authority === 'USER_ONLY') {
    return escalateToUser(question);
  }

  // 3. LEAD_CONSULT decisions go to appropriate lead
  if (authority === 'LEAD_CONSULT') {
    const lead = getLeadForCategory(question.category);

    // Dispatch lead agent via Task tool
    const response = await Task({
      subagent_type: "general-purpose",
      description: `Consult ${lead} lead`,
      prompt: `You are the ${lead}-lead agent.

        Question: ${question.text}
        Context: ${question.context}

        Follow the ${lead}-lead skill instructions.

        Output your decision in JSON format:
        {
          "lead": "${lead}",
          "decision_made": true/false,
          "decision": "...",
          "reasoning": "...",
          "authority_used": "...",
          "escalate_to": null | "user" | "cto-advisor",
          "escalation_reason": "..."
        }`
    });

    const result = parseLeadResponse(response.output);

    // Log consultation to metadata
    await logConsultation(trackId, result);

    if (result.decision_made) {
      return result.decision;
    }

    // Lead escalated - follow their recommendation
    return escalateTo(result.escalate_to, result.escalation_reason);
  }

  // 4. ORCHESTRATOR decisions are made autonomously
  return makeAutonomousDecision(question);
}

Authority Matrix Reference

See conductor/authority-matrix.md for the complete decision matrix.

Quick Reference — Always Escalate to User:

• Budget changes >$50/month
• Add/remove features from spec
• Breaking API changes
• Dependencies >50KB
• Coverage below 70%
• Security/production data changes

Quick Reference — Lead Can Decide:

• Architecture: Patterns (existing), component org, schema (additive)
• Product: Spec interpretation, copy, task order
• Tech: Dependencies <50KB, implementation approach
• QA: Coverage 70-90%, test types, mocks

---

Agent Dispatch Protocol

Dispatch with Metadata Updates

Each agent dispatch includes instructions to update metadata.json:

// Example: Dispatching executor with resumption
Task({
  subagent_type: "general-purpose",
  description: "Execute track tasks",
  prompt: `You are the loop-executor agent for track ${trackId}.

    METADATA STATE:
    - Current step: EXECUTE
    - Tasks completed: ${metadata.loop_state.checkpoints.EXECUTE.tasks_completed}
    - Last task: ${metadata.loop_state.checkpoints.EXECUTE.last_task}
    - Resume from: Next [ ] task after "${lastTask}"

    Your task:
    1. Read conductor/tracks/${trackId}/plan.md
    2. Skip all [x] tasks - they are already done
    3. Find first [ ] task after "${lastTask}"
    4. Implement following loop-executor skill
    5. After EACH task completion:
       - Mark [x] in plan.md with commit SHA
       - Update metadata.json checkpoints.EXECUTE:
         - tasks_completed++
         - last_task = "Task X.Y"
         - last_commit = "sha"
    6. Continue until all tasks complete

    MANDATORY: Update metadata.json after every task for resumption support.`
})

Agent Roster (v3)

Step	Agent	Skill	Dispatch Prompt Key Points
PRE-PLAN	Knowledge Manager	knowledge-manager	Load patterns + errors for this track type
PLAN	Planner	loop-planner	Create plan.md WITH DAG, update metadata
EVALUATE_PLAN	Plan Evaluator	loop-plan-evaluator	Run 6 checks (+ DAG + Board), write verdict
EVALUATE_PLAN	Board	board-of-directors	NEW: Full deliberation for major tracks
PARALLEL_EXECUTE	Workers	worker-templates/*	NEW: Parallel Task calls via agent-factory
EVALUATE_EXECUTION	Exec Evaluator	loop-execution-evaluator	Dispatch evaluators + quick board review
FIX	Fixer	loop-fixer	Check fix_cycle_count, implement fixes
BUSINESS_SYNC	Biz Doc Sync	business-docs-sync	Update Tier 1-3 docs if needed
POST-COMPLETE	Retrospective	retrospective-agent	Extract learnings, cleanup workers

---

Parallel Execution Engine (v3)

When to Use Parallel Execution

Parallel execution is used when:

• Plan contains dag: block with parallel_groups
• DAG validation passed in EVALUATE_PLAN
• Track has 3+ tasks that can run concurrently

PARALLEL_EXECUTE Step

async function stepParallelExecute(trackId: string, metadata: dict) {
  // 1. Initialize message bus
  const busPath = await initMessageBus(`conductor/tracks/${trackId}`);

  // 2. Parse DAG from plan.md
  const dag = await parseDagFromPlan(trackId);

  // 3. Import parallel dispatch utilities
  const { execute_parallel_phase } = require('parallel-dispatch');

  // 4. Execute all parallel groups
  const result = await execute_parallel_phase(dag, trackId, busPath, metadata);

  // 5. Update metadata with results
  metadata.loop_state.parallel_state = {
    total_workers_spawned: result.workers_spawned,
    completed_workers: result.all_tasks_completed.length,
    failed_workers: Object.keys(result.failed_tasks).length,
    parallel_groups_completed: result.parallel_groups_executed
  };

  // 6. Determine next step
  if (result.success) {
    return { next_step: 'EVALUATE_EXECUTION', status: 'PASSED' };
  } else if (result.escalate) {
    return { next_step: 'ESCALATE', reason: result.escalate_reason };
  } else {
    return { next_step: 'FIX', failures: result.failed_tasks };
  }
}

Worker Dispatch via Task Tool

Workers are dispatched using parallel Task calls:

// Dispatch 3 workers in parallel (single message, multiple tool calls)
await Promise.all([
  Task({
    subagent_type: "general-purpose",
    description: "Execute Task 1.1: Create store",
    prompt: workerPrompts["1.1"],
    run_in_background: true
  }),
  Task({
    subagent_type: "general-purpose",
    description: "Execute Task 1.2: Build resolver",
    prompt: workerPrompts["1.2"],
    run_in_background: true
  }),
  Task({
    subagent_type: "general-purpose",
    description: "Execute Task 1.3: Add validation",
    prompt: workerPrompts["1.3"],
    run_in_background: true
  })
]);

Worker Monitoring

Monitor workers via message bus polling:

async function monitorWorkers(busPath: string, taskIds: string[]) {
  const pending = new Set(taskIds);
  const completed = new Set();
  const failed = {};

  while (pending.size > 0) {
    // Check for completions
    for (const taskId of pending) {
      const eventFile = `${busPath}/events/TASK_COMPLETE_${taskId}.event`;
      if (await exists(eventFile)) {
        pending.delete(taskId);
        completed.add(taskId);
      }

      const failFile = `${busPath}/events/TASK_FAILED_${taskId}.event`;
      if (await exists(failFile)) {
        pending.delete(taskId);
        failed[taskId] = await getFailureReason(busPath, taskId);
      }
    }

    // Check for stale workers
    const stale = await checkStaleWorkers(busPath, thresholdMinutes=10);
    for (const worker of stale) {
      if (pending.has(worker.task_id)) {
        failed[worker.task_id] = `Stale: no heartbeat for ${worker.minutes_stale}m`;
        pending.delete(worker.task_id);
      }
    }

    await sleep(5000);
  }

  return { completed: [...completed], failed };
}

---

Board of Directors Integration (v3)

When to Invoke the Board

Checkpoint	Condition	Board Type
EVALUATE_PLAN	Major track (arch/integ/infra, 5+ tasks, P0)	Full meeting
EVALUATE_EXECUTION	Always	Quick review
PRE_LAUNCH	Production deploy	Security + Ops deep dive
CONFLICT	Evaluators disagree	Tie-breaker

Invoking Board at EVALUATE_PLAN

async function evaluatePlanWithBoard(trackId: string, metadata: dict) {
  // 1. Run standard plan evaluation
  const evalResult = await dispatchPlanEvaluator(trackId);

  // 2. Check if board is needed
  const needsBoard = isMajorTrack(metadata) || evalResult.recommends_board;

  if (needsBoard) {
    // 3. Invoke full board meeting
    const boardResult = await invokeBoardMeeting(
      busPath: `conductor/tracks/${trackId}/.message-bus`,
      checkpoint: "EVALUATE_PLAN",
      proposal: await readFile(`conductor/tracks/${trackId}/plan.md`),
      context: { spec: metadata.spec_summary, dag: evalResult.dag }
    );

    // 4. Store board session
    metadata.loop_state.board_sessions.push({
      session_id: boardResult.session_id,
      checkpoint: "EVALUATE_PLAN",
      verdict: boardResult.verdict,
      vote_summary: boardResult.votes,
      conditions: boardResult.conditions,
      timestamp: new Date().toISOString()
    });

    // 5. Handle board verdict
    if (boardResult.verdict === "REJECTED") {
      return {
        next_step: "PLAN",
        status: "FAILED",
        reason: "Board rejected plan",
        conditions: boardResult.conditions
      };
    }

    // Carry forward conditions for EVALUATE_EXECUTION
    metadata.board_conditions = boardResult.conditions;
  }

  return { next_step: "PARALLEL_EXECUTE", status: "PASSED" };
}

Board Quick Review at EVALUATE_EXECUTION

async function evaluateExecutionWithBoard(trackId: string, metadata: dict) {
  // 1. Run specialized evaluators
  const evalResults = await dispatchSpecializedEvaluators(trackId);

  // 2. Quick board review (no discussion phase)
  const boardReview = await invokeBoardReview(
    busPath: `conductor/tracks/${trackId}/.message-bus`,
    proposal: summarizeExecutionResults(evalResults)
  );

  // 3. Verify board conditions from EVALUATE_PLAN were met
  const conditionsMet = await verifyBoardConditions(
    metadata.board_conditions,
    evalResults
  );

  if (!conditionsMet.all_met) {
    return {
      next_step: "FIX",
      status: "FAILED",
      reason: `Board conditions not met: ${conditionsMet.unmet.join(", ")}`
    };
  }

  return evalResults.all_passed
    ? { next_step: "BUSINESS_SYNC", status: "PASSED" }
    : { next_step: "FIX", status: "FAILED" };
}

---

V3 State Machine Diagram

                              TRACK START
                                   │
                                   ▼
                    ┌──────────────────────────┐
                    │    KNOWLEDGE MANAGER     │
                    │    (Load patterns)       │
                    └────────────┬─────────────┘
                                 │
                                 ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                              PLAN (with DAG)                                 │
│  loop-planner generates plan.md with explicit dependency graph              │
└──────────────────────────────────┬──────────────────────────────────────────┘
                                   │
                                   ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                    EVALUATE_PLAN + BOARD MEETING                             │
│                                                                              │
│  1. DAG Validation (cycles, conflicts)                                       │
│  2. Standard checks (scope, overlap, deps, quality)                          │
│  3. For MAJOR tracks → invoke /board-meeting                                │
│     ┌──────────────────────────────────────────────────────────────────┐    │
│     │  BOARD DELIBERATION                                               │    │
│     │  Phase 1: All 5 directors ASSESS in parallel                      │    │
│     │  Phase 2: Directors DISCUSS via message bus                       │    │
│     │  Phase 3: Directors VOTE                                          │    │
│     │  Phase 4: RESOLVE → APPROVED / REJECTED / CONDITIONS              │    │
│     └──────────────────────────────────────────────────────────────────┘    │
│                                                                              │
│  PASS → Continue   |   FAIL → Back to PLAN with conditions                  │
└──────────────────────────────────┬──────────────────────────────────────────┘
                                   │
                                   ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                         PARALLEL_EXECUTE                                     │
│                                                                              │
│  ┌─────────────────────────────────────────────────────────────────────┐    │
│  │                        MESSAGE BUS                                   │    │
│  │  queue.jsonl | locks.json | worker-status.json | events/            │    │
│  └─────────────────────────────────────────────────────────────────────┘    │
│                                                                              │
│  For each parallel_group in DAG:                                            │
│    1. agent-factory creates specialized workers                             │
│    2. Dispatch via parallel Task(run_in_background=true)                   │
│    3. Workers coordinate via message bus:                                    │
│       - FILE_LOCK / FILE_UNLOCK for shared files                           │
│       - PROGRESS updates every 5 min                                        │
│       - TASK_COMPLETE / TASK_FAILED when done                              │
│    4. Monitor for completion, handle failures                               │
│    5. Cleanup ephemeral workers                                              │
│                                                                              │
│  ┌──────┐ ┌──────┐ ┌──────┐                                                 │
│  │Worker│ │Worker│ │Worker│  (max 5 concurrent)                            │
│  │ 1.1  │ │ 1.2  │ │ 1.3  │                                                 │
│  └──┬───┘ └──┬───┘ └──┬───┘                                                 │
│     └────────┴────────┘                                                      │
│              │                                                               │
│  PASS → Continue   |   PARTIAL_FAIL → Isolate + Continue                    │
└──────────────────────────────────┬──────────────────────────────────────────┘
                                   │
                                   ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                 EVALUATE_EXECUTION + BOARD REVIEW                            │
│                                                                              │
│  1. Specialized evaluators (UI, Code, Integration, Business)                │
│  2. Quick board review (no discussion)                                       │
│  3. Verify board conditions from EVALUATE_PLAN                              │
│                                                                              │
│  PASS → BUSINESS_SYNC? → COMPLETE                                           │
│  FAIL → FIX (with specific failures)                                        │
└──────────────────────────────────┬──────────────────────────────────────────┘
                                   │
                              ┌────┴────┐
                              │         │
                         PASS ▼    FAIL ▼
                    ┌──────────┐  ┌──────────┐
                    │BUSINESS  │  │   FIX    │
                    │  SYNC    │  │ (max 3x) │
                    └────┬─────┘  └────┬─────┘
                         │             │
                         ▼             │
                    ┌──────────┐       │
                    │ COMPLETE │◄──────┘
                    │          │   (after fix passes)
                    └────┬─────┘
                         │
                         ▼
                    ┌──────────────────────────┐
                    │   RETROSPECTIVE AGENT    │
                    │   + Cleanup workers      │
                    └──────────────────────────┘

---

Resumption Protocol

When orchestrator starts, it resumes from exact state:

async function resumeOrchestration(trackId: string) {
  const { current_step, step_status, metadata } = await detectCurrentStep(trackId);

  switch (step_status) {
    case 'NOT_STARTED':
      // Start the step fresh
      return dispatchAgent(current_step, metadata);

    case 'IN_PROGRESS':
      // Resume the step with checkpoint data
      const checkpoint = metadata.loop_state.checkpoints[current_step];
      return resumeAgent(current_step, checkpoint);

    case 'PASSED':
      // Move to next step
      const nextStep = getNextStep(current_step, 'PASS');
      await updateMetadata(trackId, { current_step: nextStep, step_status: 'NOT_STARTED' });
      return dispatchAgent(nextStep, metadata);

    case 'FAILED':
      // Handle based on which step failed
      if (current_step === 'EVALUATE_PLAN') {
        await updateMetadata(trackId, { current_step: 'PLAN', step_status: 'NOT_STARTED' });
        return dispatchAgent('PLAN', metadata);
      }
      if (current_step === 'EVALUATE_EXECUTION') {
        // Check fix cycle limit
        if (metadata.loop_state.fix_cycle_count >= 3) {
          return escalateToUser('Fix cycle limit exceeded after 3 attempts');
        }
        await updateMetadata(trackId, {
          current_step: 'FIX',
          step_status: 'NOT_STARTED',
          fix_cycle_count: metadata.loop_state.fix_cycle_count + 1
        });
        return dispatchAgent('FIX', metadata);
      }

    case 'BLOCKED':
      // Check if blocker is resolved
      const activeBlockers = metadata.blockers.filter(b => b.status === 'ACTIVE');
      if (activeBlockers.length > 0) {
        return escalateToUser(`Track blocked: ${activeBlockers[0].description}`);
      }
      // Blocker resolved, continue
      await updateMetadata(trackId, { step_status: 'NOT_STARTED' });
      return dispatchAgent(current_step, metadata);
  }
}

Resumption by Step

Step	Resumption Data	Action
PLAN	checkpoints.PLAN.plan_version	Re-run planner if revising
EXECUTE	checkpoints.EXECUTE.last_task	Skip completed tasks, continue from next
FIX	checkpoints.FIX.fixes_remaining	Continue with remaining fixes

---

The Full Loop (Automated)

┌─────────────────────────────────────────────────────────────────┐
│                        ORCHESTRATOR                             │
│                                                                 │
│  1. Read metadata.json → detect current_step + step_status      │
│  2. Dispatch appropriate agent via Task tool                    │
│  3. Agent updates metadata.json checkpoints                     │
│  4. Agent returns → orchestrator reads new state                │
│  5. Continue to next step or handle failure                     │
│  6. Loop until COMPLETE or escalation needed                    │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

PLAN ──► EVALUATE_PLAN ──► EXECUTE ──► EVALUATE_EXECUTION
  ▲            │                              │
  │        FAIL → back                   PASS → BUSINESS_SYNC? → COMPLETE
  │                                      FAIL → FIX
  │                                             │
  └─────────────────────────────────────────────┘
                    (after fix, re-evaluate)

---

Escalation Triggers

Escalate to user (stop the loop) when:

1. Fix cycle limit — 3 failed EVALUATE → FIX cycles
2. USER_ONLY decision — From authority matrix
3. Lead escalated — Lead returned escalate_to: "user"
4. Blocker detected — External dependency blocking progress
5. Max iterations — Safety limit of 50 loop iterations reached

Escalation Format

## Orchestrator Paused — User Input Required

**Track**: [track-id]
**Current Step**: [step]
**Reason**: [escalation reason]

**Context**:
[What was happening when escalation triggered]

**Options**:
1. [Option 1]
2. [Option 2]
3. [Option 3 if applicable]

What would you like to do?

---

Track Completion Protocol

When current_step reaches COMPLETE:

1. Update metadata.json

{
  "status": "complete",
  "completed_at": "[timestamp]",
  "loop_state": {
    "current_step": "COMPLETE",
    "step_status": "PASSED"
  }
}

2. Update tracks.md — Move track to "Done" table with date

3. Update conductor/index.md — Update current status

4. Commit — docs: complete [track-id] - evaluation passed

5. Report to user

6. Run Retrospective (after completion commit): Dispatch agent: "Read conductor/tracks/{trackId}/plan.md and git log. Extract reusable patterns → append to conductor/knowledge/patterns.md Extract error fixes → append to conductor/knowledge/errors.json Create files if they don't exist."

## Track Complete

**Track**: [track-id]
**Phases**: [count] completed
**Tasks**: [count] completed
**Evaluation**: PASS — all checks passed
**Lead Consultations**: [count] decisions made autonomously
**Commits**: [list of key commits]

**Next track**: [suggest from tracks.md]

---

CTO Advisor Integration

For technical tracks, automatically include CTO review during EVALUATE_PLAN:

// Detect if track is technical
const technicalKeywords = [
  'architecture', 'system design', 'integration', 'API', 'database',
  'schema', 'migration', 'infrastructure', 'scalability', 'performance',
  'security', 'authentication', 'authorization', 'deployment'
];

const isTechnical = technicalKeywords.some(keyword =>
  spec.toLowerCase().includes(keyword) || plan.toLowerCase().includes(keyword)
);

if (isTechnical) {
  // Include CTO review in plan evaluation
  dispatchPrompt += `
    This is a TECHNICAL track. Your evaluation must include:
    1. Standard plan checks (scope, overlap, dependencies, clarity)
    2. CTO technical review using cto-plan-reviewer skill

    Both must PASS for plan evaluation to pass.`;
}

---

Learning Layer Integration

The orchestrator integrates the Knowledge Layer for continuous learning:

Pre-Planning: Knowledge Manager

BEFORE dispatching the planner, run Knowledge Manager to load relevant patterns:

async function dispatchPlannerWithKnowledge(trackId: string) {
  // 1. Run Knowledge Manager first
  const knowledgeBrief = await Task({
    subagent_type: "general-purpose",
    description: "Load knowledge for track",
    prompt: `You are the knowledge-manager agent.

      Track: ${trackId}
      Spec: ${await readFile(`conductor/tracks/${trackId}/spec.md`)}

      1. Extract keywords from the spec
      2. Search conductor/knowledge/patterns.md for matching patterns
      3. Search conductor/knowledge/errors.json for relevant errors
      4. Return a knowledge brief with:
         - Relevant patterns to apply
         - Known errors to avoid
         - Similar previous tracks (if any)

      Follow .claude/skills/knowledge/knowledge-manager/SKILL.md`
  });

  // 2. Dispatch planner WITH knowledge brief injected
  await Task({
    subagent_type: "general-purpose",
    description: "Create track plan",
    prompt: `You are the loop-planner agent for track ${trackId}.

      ## KNOWLEDGE BRIEF (from previous tracks)
      ${knowledgeBrief.output}

      ## YOUR TASK
      Create plan.md using the patterns above where applicable.
      Avoid the known errors listed.

      Follow .claude/skills/loop-planner/SKILL.md`
  });
}

Post-Completion: Retrospective Agent

AFTER a track reaches COMPLETE, run Retrospective Agent to extract learnings:

async function runPostCompletionRetrospective(trackId: string) {
  await Task({
    subagent_type: "general-purpose",
    description: "Run track retrospective",
    prompt: `You are the retrospective-agent.

      Track: ${trackId}

      1. Read conductor/tracks/${trackId}/plan.md (all tasks and fix cycles)
      2. Read conductor/tracks/${trackId}/metadata.json (fix counts, consultations)
      3. Analyze: What worked? What failed? What patterns emerged?
      4. Update conductor/knowledge/patterns.md with new reusable solutions
      5. Update conductor/knowledge/errors.json with new error patterns
      6. Write retrospective to conductor/tracks/${trackId}/retrospective.md
      7. Propose skill improvements if workflow issues found

      Follow .claude/skills/knowledge/retrospective-agent/SKILL.md`
  });
}

Updated State Machine with Learning

                              TRACK START
                                   │
                                   ▼
                    ┌──────────────────────────┐
                    │    KNOWLEDGE MANAGER     │  ◄── NEW: Load patterns & errors
                    │    (Pre-planning intel)  │
                    └────────────┬─────────────┘
                                 │
                                 ▼
PLAN ──► EVALUATE_PLAN ──► EXECUTE ──► EVALUATE_EXECUTION
  ▲            │                              │
  │        FAIL → back                   PASS → BUSINESS_SYNC? → COMPLETE
  │                                      FAIL → FIX                  │
  │                                             │                    │
  └─────────────────────────────────────────────┘                    │
                                                                     ▼
                                                    ┌──────────────────────────┐
                                                    │   RETROSPECTIVE AGENT    │  ◄── NEW
                                                    │   (Extract learnings)    │
                                                    └────────────┬─────────────┘
                                                                 │
                                                                 ▼
                                                    ┌──────────────────────────┐
                                                    │    KNOWLEDGE BASE        │
                                                    │  patterns.md + errors.json│
                                                    └──────────────────────────┘
                                                                 │
                                                                 ▼
                                                          NEXT TRACK
                                                    (now smarter than before)

Knowledge Layer Files

File	Purpose	Updated By
conductor/knowledge/patterns.md	Reusable solutions	Retrospective Agent
conductor/knowledge/errors.json	Error → Fix registry	Retrospective Agent, Fixer
conductor/tracks/[id]/retrospective.md	Track-specific learnings	Retrospective Agent

Fixer Integration with Error Registry

The loop-fixer also uses the error registry:

// In loop-fixer, before attempting a fix
async function findKnownSolution(errorMessage: string) {
  const errors = JSON.parse(await readFile('conductor/knowledge/errors.json'));

  for (const error of errors.errors) {
    if (new RegExp(error.pattern, 'i').test(errorMessage)) {
      return {
        found: true,
        solution: error.solution,
        code_fix: error.code_fix
      };
    }
  }

  return { found: false };
}

// After fixing a new error, log it
async function logNewError(pattern, solution, trackId) {
  const errors = JSON.parse(await readFile('conductor/knowledge/errors.json'));
  errors.errors.push({
    id: `err-${String(errors.errors.length + 1).padStart(3, '0')}`,
    pattern,
    solution,
    discovered_in: trackId,
    last_seen: new Date().toISOString().split('T')[0]
  });
  await writeFile('conductor/knowledge/errors.json', JSON.stringify(errors, null, 2));
}

---

Quick Reference

Starting a Track

User: /conductor implement

Orchestrator:
1. Read conductor/tracks.md → get active track
2. Read conductor/tracks/[track]/metadata.json → get loop_state
3. Determine current step and status
4. Dispatch appropriate agent
5. Loop until complete

State Locations

Data	Location	Purpose
Loop state	metadata.json → loop_state	Primary state machine
Task progress	plan.md markers	Human-readable progress
Lead decisions	metadata.json → lead_consultations	Decision audit trail
Blockers	metadata.json → blockers	Escalation tracking
Authority rules	conductor/authority-matrix.md	Decision boundaries

Files Modified by Orchestrator

• conductor/tracks/[track]/metadata.json — State updates
• conductor/tracks.md — Completion tracking
• conductor/index.md — Current status