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GOAP Execution Plan: AI Agent Enhancement v2

GOAP Execution Plan: AI Agent Enhancement v2

Meta Information

  • Swarm ID: swarm_1764655531178_udtox74dx
  • Methodology: GOAP + SPARC
  • Generated: 2025-12-01
  • Planner: Goal-Oriented Action Planning Specialist

1. STATE SPACE ANALYSIS

Current World State (T₀)

has_basic_agent: true
has_memory_system: true
has_ruvector_modules: true
has_task_planner: false
has_change_preview: false
has_ruvector_integration: false
has_site_context: false
has_approval_workflow: false
agent_handles_complex_nlp: false
user_sees_plan_before_execution: false
can_classify_intents: false
can_generate_actions: false
can_generate_diffs: false
can_format_previews: false
can_search_semantically: false
can_query_graph: false
can_get_recommendations: false
knows_site_structure: false
knows_content_schemas: false
has_workflow_states: false
all_modules_tested: false
documentation_complete: false

Goal World State (T_final)

has_basic_agent: true
has_memory_system: true
has_ruvector_modules: true
has_task_planner: true
has_change_preview: true
has_ruvector_integration: true
has_site_context: true
has_approval_workflow: true
agent_handles_complex_nlp: true
user_sees_plan_before_execution: true
all_modules_tested: true
documentation_complete: true

2. ACTION DEPENDENCY GRAPH

LAYER 0 (Foundational - No Dependencies)
├── create_task_planner [Cost: 5]
├── create_change_preview [Cost: 4]
├── create_ruvector_bridge [Cost: 4]
└── create_site_context [Cost: 4]

LAYER 1 (Depends on Layer 0)
├── create_intent_classifier [Cost: 3] → depends: create_task_planner
├── create_diff_generator [Cost: 2] → depends: create_change_preview
├── integrate_search [Cost: 2] → depends: create_ruvector_bridge
├── integrate_graph [Cost: 2] → depends: create_ruvector_bridge
└── analyze_site_structure [Cost: 2] → depends: create_site_context

LAYER 2 (Depends on Layer 1)
├── create_action_generator [Cost: 3] → depends: create_intent_classifier
├── create_preview_formatter [Cost: 2] → depends: create_diff_generator
├── integrate_recommendations [Cost: 2] → depends: integrate_search
└── create_schema_detector [Cost: 2] → depends: analyze_site_structure

LAYER 3 (Convergence)
└── create_approval_workflow [Cost: 4]
    → depends: create_task_planner, create_change_preview

LAYER 4 (State Machine)
└── create_state_machine [Cost: 2]
    → depends: create_approval_workflow

LAYER 5 (Integration)
└── integrate_all_modules [Cost: 4]
    → depends: all Layer 0-4 complete

LAYER 6 (Testing - Parallel)
├── test_task_planner [Cost: 2]
├── test_change_preview [Cost: 2]
├── test_ruvector_bridge [Cost: 2]
├── test_site_context [Cost: 2]
└── test_approval_workflow [Cost: 2]

LAYER 7 (Documentation)
└── write_documentation [Cost: 2]
    → depends: all tests pass

Total Actions: 24 Critical Path Length: 8 layers Maximum Parallelism: 4 actions (Layer 0)

3. OPTIMAL EXECUTION SEQUENCE (A* RESULT)

Heuristic Function

h(state) = Σ(unsatisfied_goals) × minimum_remaining_cost
Cost(path) = g(n) + h(n)

Optimal Path (Total Cost: 55)

Phase 1: Foundation (Cost: 17, Time: ~28h)

Execute in Parallel:

├─ A1: create_task_planner [Cost: 5, 7h SPARC]
├─ A2: create_change_preview [Cost: 4, 5h SPARC]
├─ A3: create_ruvector_bridge [Cost: 4, 5h SPARC]
└─ A4: create_site_context [Cost: 4, 5h SPARC]

State After Phase 1:

has_task_planner: true
has_change_preview: true
has_ruvector_integration: true (partial)
has_site_context: true

Phase 2: Specialization (Cost: 11, Time: ~16h)

Execute in Parallel:

├─ A5: create_intent_classifier [Cost: 3, 4h]
├─ A6: create_diff_generator [Cost: 2, 3h]
├─ A7: integrate_search [Cost: 2, 3h]
├─ A8: integrate_graph [Cost: 2, 3h]
└─ A9: analyze_site_structure [Cost: 2, 3h]

State After Phase 2:

can_classify_intents: true
can_generate_diffs: true
can_search_semantically: true
can_query_graph: true
knows_site_structure: true

Phase 3: Advanced Features (Cost: 9, Time: ~12h)

Execute in Parallel:

├─ A10: create_action_generator [Cost: 3, 4h]
├─ A11: create_preview_formatter [Cost: 2, 3h]
├─ A12: integrate_recommendations [Cost: 2, 3h]
└─ A13: create_schema_detector [Cost: 2, 2h]

State After Phase 3:

can_generate_actions: true
can_format_previews: true
can_get_recommendations: true
knows_content_schemas: true

Phase 4: Workflow Integration (Cost: 6, Time: ~9h)

Execute Sequentially:

A14: create_approval_workflow [Cost: 4, 5h SPARC]
  └─> A15: create_state_machine [Cost: 2, 4h]

State After Phase 4:

has_approval_workflow: true
has_workflow_states: true

Phase 5: System Integration (Cost: 4, Time: ~6h)

Execute:

A16: integrate_all_modules [Cost: 4, 6h]

State After Phase 5:

agent_handles_complex_nlp: true
user_sees_plan_before_execution: true

Phase 6: Quality Assurance (Cost: 10, Time: ~15h)

Execute in Parallel:

├─ A17: test_task_planner [Cost: 2, 3h]
├─ A18: test_change_preview [Cost: 2, 3h]
├─ A19: test_ruvector_bridge [Cost: 2, 3h]
├─ A20: test_site_context [Cost: 2, 3h]
└─ A21: test_approval_workflow [Cost: 2, 3h]

State After Phase 6:

all_modules_tested: true

Phase 7: Documentation (Cost: 2, Time: ~3h)

Execute:

A22: write_documentation [Cost: 2, 3h]

Final State: GOAL ACHIEVED ✓

4. SPARC PHASE BREAKDOWN

Major Modules (SPARC Applied)

Task Planner (7h)

S: Specification (2h)
  - Define intent taxonomy (ADD, UPDATE, DELETE, SEARCH, QUERY, ANALYZE)
  - Design action generation pipeline
  - Specify dependency resolution

P: Pseudocode (1h)
  - Intent classifier algorithm
  - Action sequencer logic
  - Dependency graph builder

A: Architecture (1h)
  - Module structure: IntentClassifier, ActionGenerator, DependencyResolver
  - Integration points with memory system
  - Event emission patterns

R: Refinement (2h - TDD)
  - Test: "classify user intent from NLP input"
  - Test: "generate ordered action sequence"
  - Test: "resolve action dependencies"
  - Implement until green

C: Completion (1h)
  - Integration with existing agent
  - Error handling
  - Logging and observability

Change Preview (5h)

S: Specification (1h)
  - Define diff generation strategies
  - Design preview formatting
  - Specify approval mechanism

P: Pseudocode (1h)
  - File diff algorithm
  - Syntax-aware formatting
  - Preview renderer

A: Architecture (1h)
  - DiffGenerator, PreviewFormatter, ApprovalHandler
  - Integration with file system

R: Refinement (1h - TDD)
  - Test: "generate accurate file diffs"
  - Test: "format diffs for readability"
  - Test: "handle approval workflow"

C: Completion (1h)
  - CLI integration
  - User feedback mechanisms

Ruvector Bridge (5h)

S: Specification (1h)
  - Define bridge API surface
  - Design semantic search integration
  - Specify graph query patterns

P: Pseudocode (1h)
  - Bridge initialization
  - Search adapter
  - Graph adapter
  - Recommendation engine

A: Architecture (1h)
  - RuvectorBridge class
  - Adapter pattern for each module
  - Result normalization

R: Refinement (1h - TDD)
  - Test: "semantic search returns relevant results"
  - Test: "graph queries resolve relationships"
  - Test: "recommendations are contextual"

C: Completion (1h)
  - Performance optimization
  - Caching layer

Site Context (5h)

S: Specification (1h)
  - Define site structure analysis
  - Design schema detection
  - Specify context caching

P: Pseudocode (1h)
  - Site crawler
  - Schema detector
  - Context builder

A: Architecture (1h)
  - SiteAnalyzer, SchemaDetector, ContextCache
  - File system integration

R: Refinement (1h - TDD)
  - Test: "detect site structure correctly"
  - Test: "identify content schemas"
  - Test: "cache context efficiently"

C: Completion (1h)
  - Incremental updates
  - Change detection

Approval Workflow (5h)

S: Specification (1h)
  - Define workflow states: DRAFT, PREVIEW, APPROVED, REJECTED, EXECUTING, COMPLETE
  - Design state transition rules
  - Specify rollback mechanisms

P: Pseudocode (1h)
  - State machine implementation
  - Transition validator
  - Event emitter

A: Architecture (1h)
  - WorkflowStateMachine class
  - Event-driven architecture
  - State persistence

R: Refinement (1h - TDD)
  - Test: "valid state transitions succeed"
  - Test: "invalid transitions are rejected"
  - Test: "rollback restores previous state"

C: Completion (1h)
  - Audit logging
  - Notification system

5. PARALLEL EXECUTION PLAN

Concurrency Matrix

Phase Max Parallel Actions Wall Time
1 4 A1-A4 ~7h
2 5 A5-A9 ~4h
3 4 A10-A13 ~4h
4 1 A14-A15 ~9h
5 1 A16 ~6h
6 5 A17-A21 ~3h
7 1 A22 ~3h

Total Sequential Time: 94 hours Total Parallel Time: ~36 hours Speedup: 2.6x

Agent Assignment Strategy

Phase 1 (4 agents in parallel):
  Agent-1 (researcher) → create_task_planner
  Agent-2 (coder) → create_change_preview
  Agent-3 (coder) → create_ruvector_bridge
  Agent-4 (code-analyzer) → create_site_context

Phase 2 (5 agents in parallel):
  Agent-1 (researcher) → create_intent_classifier
  Agent-2 (coder) → create_diff_generator
  Agent-3 (coder) → integrate_search
  Agent-4 (coder) → integrate_graph
  Agent-5 (code-analyzer) → analyze_site_structure

Phase 3 (4 agents in parallel):
  Agent-1 (coder) → create_action_generator
  Agent-2 (coder) → create_preview_formatter
  Agent-3 (coder) → integrate_recommendations
  Agent-4 (code-analyzer) → create_schema_detector

Phase 4 (1 agent sequential):
  Agent-1 (system-architect) → create_approval_workflow
  Agent-1 (system-architect) → create_state_machine

Phase 5 (1 agent):
  Agent-1 (system-architect) → integrate_all_modules

Phase 6 (5 agents in parallel):
  Agent-1 (tester) → test_task_planner
  Agent-2 (tester) → test_change_preview
  Agent-3 (tester) → test_ruvector_bridge
  Agent-4 (tester) → test_site_context
  Agent-5 (tester) → test_approval_workflow

Phase 7 (1 agent):
  Agent-1 (api-docs) → write_documentation

6. MILESTONES & ACCEPTANCE CRITERIA

Milestone 1: Foundation Complete (Day 1)

Criteria:

  • ✓ Task planner module exists with intent classification
  • ✓ Change preview generates file diffs
  • ✓ Ruvector bridge connects to all modules
  • ✓ Site context analyzes project structure
  • ✓ All modules have basic tests (>70% coverage)

Deliverables:

  • task-planner.js
  • change-preview.js
  • ruvector-bridge.js
  • site-context.js

Milestone 2: Specialization Complete (Day 2)

Criteria:

  • ✓ Intent classifier achieves >85% accuracy on test intents
  • ✓ Diff generator handles all file types
  • ✓ Semantic search returns relevant results
  • ✓ Graph queries resolve page relationships
  • ✓ Site structure analysis identifies all pages/components

Deliverables:

  • Intent taxonomy document
  • Diff examples for all file types
  • Search result quality metrics

Milestone 3: Advanced Features Complete (Day 3)

Criteria:

  • ✓ Action generator produces valid action sequences
  • ✓ Preview formatter outputs readable diffs
  • ✓ Recommendations are contextually relevant
  • ✓ Schema detector identifies content patterns

Deliverables:

  • Action sequence examples
  • Preview rendering samples
  • Schema detection report

Milestone 4: Workflow Integration Complete (Day 4)

Criteria:

  • ✓ Approval workflow implements all states
  • ✓ State machine validates transitions
  • ✓ Rollback mechanism tested
  • ✓ Event system publishes workflow events

Deliverables:

  • State diagram
  • Workflow API documentation
  • Event schema

Milestone 5: System Integration Complete (Day 5)

Criteria:

  • ✓ All modules integrate seamlessly
  • ✓ End-to-end user flows work
  • ✓ Agent handles complex multi-step requests
  • ✓ User sees preview before execution

Deliverables:

  • Integration test suite
  • User flow documentation
  • Demo scenarios

Milestone 6: Quality Assurance Complete (Day 6)

Criteria:

  • ✓ All module tests pass
  • ✓ Integration tests pass
  • ✓ Test coverage >80% overall
  • ✓ Performance benchmarks meet targets
  • ✓ Error handling validated

Deliverables:

  • Test coverage report
  • Performance benchmark results
  • Error handling documentation

Milestone 7: Documentation Complete (Day 7)

Criteria:

  • ✓ API documentation complete
  • ✓ User guide written
  • ✓ Architecture diagrams created
  • ✓ Examples for all features
  • ✓ Troubleshooting guide

Deliverables:

  • API reference
  • User guide
  • Architecture documentation
  • Example gallery

7. CRITICAL ALGORITHMS (Pseudocode)

Algorithm 1: Intent Classifier

function classifyIntent(naturalLanguageInput) {
  // Tokenize and extract features
  tokens = tokenize(input)
  features = extractFeatures(tokens)

  // Pattern matching against intent taxonomy
  intentScores = {}
  for (intent in INTENT_TAXONOMY) {
    score = calculateSimilarity(features, intent.patterns)
    intentScores[intent] = score
  }

  // Select best match with confidence threshold
  bestIntent = argmax(intentScores)
  confidence = intentScores[bestIntent]

  if (confidence < CONFIDENCE_THRESHOLD) {
    return { intent: 'CLARIFY', confidence: 0, suggestions: topK(intentScores, 3) }
  }

  // Extract entities (file names, content, selectors, etc.)
  entities = extractEntities(tokens, bestIntent)

  return {
    intent: bestIntent,
    confidence: confidence,
    entities: entities
  }
}

Algorithm 2: Action Generator

function generateActionSequence(intent, entities, siteContext) {
  actions = []

  switch (intent) {
    case 'ADD_CONTENT':
      // Check if page exists
      if (!siteContext.pageExists(entities.page)) {
        actions.push({ type: 'CREATE_PAGE', params: { page: entities.page } })
      }
      actions.push({ type: 'INSERT_CONTENT', params: entities })
      actions.push({ type: 'UPDATE_INDEX', params: { page: entities.page } })
      break

    case 'UPDATE_STYLE':
      // Search for existing styles
      existingStyles = siteContext.findStyles(entities.selector)
      if (existingStyles.length > 0) {
        actions.push({ type: 'MODIFY_STYLE', params: { ...entities, existing: existingStyles } })
      } else {
        actions.push({ type: 'ADD_STYLE', params: entities })
      }
      break

    case 'SEARCH_CONTENT':
      // Use semantic search
      actions.push({ type: 'SEMANTIC_SEARCH', params: { query: entities.query } })
      actions.push({ type: 'PRESENT_RESULTS', params: {} })
      break
  }

  // Resolve dependencies between actions
  actions = resolveDependencies(actions)

  // Estimate costs
  for (action of actions) {
    action.cost = estimateCost(action)
  }

  return actions
}

Algorithm 3: Change Preview Generator

function generateChangePreview(actions, currentState) {
  previews = []

  for (action of actions) {
    preview = {
      action: action,
      before: null,
      after: null,
      diff: null
    }

    switch (action.type) {
      case 'MODIFY_FILE':
        preview.before = readFile(action.params.file)
        preview.after = applyModification(preview.before, action.params.changes)
        preview.diff = generateDiff(preview.before, preview.after)
        break

      case 'CREATE_FILE':
        preview.before = null
        preview.after = action.params.content
        preview.diff = { type: 'CREATE', lines: preview.after.split('\n').length }
        break

      case 'DELETE_FILE':
        preview.before = readFile(action.params.file)
        preview.after = null
        preview.diff = { type: 'DELETE', lines: preview.before.split('\n').length }
        break
    }

    previews.push(preview)
  }

  return formatPreviews(previews)
}

function generateDiff(before, after) {
  // Unified diff format
  beforeLines = before.split('\n')
  afterLines = after.split('\n')

  lcs = longestCommonSubsequence(beforeLines, afterLines)

  diff = []
  i = 0, j = 0

  for (line of lcs) {
    // Add deletions
    while (beforeLines[i] !== line) {
      diff.push({ type: '-', line: beforeLines[i], lineNum: i + 1 })
      i++
    }

    // Add additions
    while (afterLines[j] !== line) {
      diff.push({ type: '+', line: afterLines[j], lineNum: j + 1 })
      j++
    }

    // Add unchanged
    diff.push({ type: ' ', line: line, lineNum: i + 1 })
    i++
    j++
  }

  return diff
}

Algorithm 4: Approval Workflow State Machine

const STATES = ['DRAFT', 'PREVIEW', 'APPROVED', 'REJECTED', 'EXECUTING', 'COMPLETE', 'FAILED', 'ROLLED_BACK']

const TRANSITIONS = {
  DRAFT: ['PREVIEW'],
  PREVIEW: ['APPROVED', 'REJECTED', 'DRAFT'],
  APPROVED: ['EXECUTING'],
  REJECTED: ['DRAFT'],
  EXECUTING: ['COMPLETE', 'FAILED'],
  COMPLETE: [],
  FAILED: ['ROLLED_BACK', 'DRAFT'],
  ROLLED_BACK: ['DRAFT']
}

class WorkflowStateMachine {
  constructor(workflowId) {
    this.workflowId = workflowId
    this.currentState = 'DRAFT'
    this.history = [{ state: 'DRAFT', timestamp: Date.now() }]
    this.snapshot = null
  }

  transition(targetState, reason = null) {
    // Validate transition
    if (!TRANSITIONS[this.currentState].includes(targetState)) {
      throw new Error(`Invalid transition: ${this.currentState} -> ${targetState}`)
    }

    // Execute pre-transition hooks
    this.executePreHooks(this.currentState, targetState)

    // Take snapshot before critical states
    if (targetState === 'EXECUTING') {
      this.snapshot = this.takeSnapshot()
    }

    // Update state
    const previousState = this.currentState
    this.currentState = targetState
    this.history.push({
      state: targetState,
      timestamp: Date.now(),
      from: previousState,
      reason: reason
    })

    // Execute post-transition hooks
    this.executePostHooks(previousState, targetState)

    // Emit event
    this.emit('state-changed', {
      workflow: this.workflowId,
      from: previousState,
      to: targetState,
      reason: reason
    })
  }

  rollback() {
    if (!this.snapshot) {
      throw new Error('No snapshot available for rollback')
    }

    restoreSnapshot(this.snapshot)
    this.transition('ROLLED_BACK', 'Execution failed, snapshot restored')
  }
}

Algorithm 5: Semantic Search Integration

function semanticSearch(query, context) {
  // Vectorize query
  queryVector = vectorizeText(query)

  // Search using ruvector
  results = ruvector.search({
    vector: queryVector,
    limit: 20,
    threshold: 0.7
  })

  // Re-rank using context
  scoredResults = []
  for (result of results) {
    contextScore = calculateContextRelevance(result, context)
    finalScore = 0.6 * result.score + 0.4 * contextScore
    scoredResults.push({ ...result, finalScore: finalScore })
  }

  // Sort and filter
  rankedResults = scoredResults
    .sort((a, b) => b.finalScore - a.finalScore)
    .filter(r => r.finalScore > 0.6)
    .slice(0, 10)

  return rankedResults
}

function calculateContextRelevance(result, context) {
  score = 0.0

  // Boost if in same directory
  if (result.file.startsWith(context.currentDir)) {
    score += 0.3
  }

  // Boost if same file type
  if (result.fileType === context.currentFileType) {
    score += 0.2
  }

  // Boost if recently modified
  daysSinceModified = (Date.now() - result.modified) / (1000 * 60 * 60 * 24)
  if (daysSinceModified < 7) {
    score += 0.3 * (1 - daysSinceModified / 7)
  }

  // Boost if in user's working set
  if (context.workingSet.includes(result.file)) {
    score += 0.4
  }

  return Math.min(score, 1.0)
}

8. RISK ANALYSIS & CONTINGENCIES

High-Risk Areas

Risk 1: Intent Classification Accuracy

Likelihood: Medium Impact: High Mitigation:

  • Build comprehensive test dataset (100+ examples)
  • Implement fallback to clarification flow
  • Add user feedback loop for training
  • Use few-shot learning with Claude API

Contingency:

  • If accuracy <80%, pivot to rule-based classification
  • Implement hybrid approach (rules + ML)

Risk 2: Ruvector Integration Complexity

Likelihood: Medium Impact: Medium Mitigation:

  • Start with simple search, add features incrementally
  • Mock ruvector responses for testing
  • Design bridge with adapter pattern for easy swap

Contingency:

  • If ruvector has issues, use basic text search initially
  • Implement abstraction layer to swap search backends

Risk 3: Approval Workflow State Explosion

Likelihood: Low Impact: Medium Mitigation:

  • Keep state machine simple (8 states max)
  • Design clear transition rules
  • Extensive state machine testing

Contingency:

  • If states become unmanageable, simplify to: DRAFT, PENDING, APPROVED, EXECUTING, DONE

Risk 4: Performance Degradation

Likelihood: Medium Impact: Medium Mitigation:

  • Implement caching at every layer
  • Use lazy loading for site context
  • Profile and optimize hot paths

Contingency:

  • If performance issues arise, add dedicated optimization phase
  • Implement async/streaming for large operations

Risk 5: Agent Coordination Failures

Likelihood: Low Impact: High Mitigation:

  • Use Claude Flow hooks consistently
  • Implement health checks
  • Clear agent handoff protocols

Contingency:

  • If coordination breaks, fall back to sequential execution
  • Manual intervention points defined

9. RESOURCE ESTIMATES

Agent Hours by Phase

Phase Agent Type Hours Count Total
1 Mixed 7 4 28
2 Mixed 4 5 20
3 Coder 4 4 16
4 Architect 9 1 9
5 Architect 6 1 6
6 Tester 3 5 15
7 Docs 3 1 3

Total Agent Hours: 97 hours Wall Clock Time: ~36 hours (with parallelism) Calendar Time: ~5 business days

Token Estimates

Operation Tokens/Op Count Total
Intent Classification 2,000 100 200K
Code Generation 8,000 24 192K
Code Review 5,000 24 120K
Test Generation 4,000 24 96K
Documentation 10,000 7 70K

Total Estimated Tokens: ~680K tokens Cost (Sonnet 4.5): ~$2.04 input + $6.10 output = ~$8.14

10. SUCCESS METRICS

Quantitative Metrics

  • Intent Classification Accuracy: >85%
  • Test Coverage: >80%
  • Response Time: <2s for intent classification, <5s for action generation
  • Preview Generation: <1s per file
  • Semantic Search Relevance: >0.7 average score

Qualitative Metrics

  • User Satisfaction: Agent correctly interprets complex requests
  • Code Quality: Modules follow established patterns
  • Maintainability: Clear separation of concerns
  • Documentation: Complete and understandable

Acceptance Criteria for Completion

  1. ✓ All 7 milestones achieved
  2. ✓ All tests passing (>80% coverage)
  3. ✓ Documentation complete
  4. ✓ Demo scenarios working end-to-end
  5. ✓ Performance benchmarks met
  6. ✓ User can issue complex multi-step requests
  7. ✓ Agent shows preview before execution
  8. ✓ Approval workflow handles all edge cases

11. EXECUTION CHECKLIST

Pre-Execution

  • Swarm topology initialized (mesh, 6 agents)
  • Agent roles assigned
  • Memory namespaces created
  • Session ID registered
  • Backup of current agent code

Phase 1 Checklist

  • Task planner module created
  • Change preview module created
  • Ruvector bridge module created
  • Site context module created
  • All Phase 1 tests passing

Phase 2 Checklist

  • Intent classifier implemented
  • Diff generator implemented
  • Search integration complete
  • Graph integration complete
  • Site structure analysis working

Phase 3 Checklist

  • Action generator implemented
  • Preview formatter implemented
  • Recommendations integrated
  • Schema detector implemented

Phase 4 Checklist

  • Approval workflow created
  • State machine implemented
  • Event system working

Phase 5 Checklist

  • All modules integrated
  • End-to-end flows working

Phase 6 Checklist

  • All module tests passing
  • Integration tests passing
  • Performance benchmarks met

Phase 7 Checklist

  • API documentation complete
  • User guide written
  • Architecture diagrams created

12. NEXT ACTIONS

Immediate (Next 10 minutes)

  1. Initialize swarm with mesh topology
  2. Spawn 4 agents for Phase 1
  3. Distribute task specifications to agents
  4. Setup memory namespaces for coordination

Short-term (Next 2 hours)

  1. Agents begin SPARC specification phase
  2. Monitor progress via hooks
  3. Resolve any blockers
  4. Begin Phase 1 parallel execution

Medium-term (Next 24 hours)

  1. Complete Phase 1 (Foundation)
  2. Begin Phase 2 (Specialization)
  3. First milestone review

APPENDIX A: State Transition Table

From State To State Condition Action
DRAFT PREVIEW User requests preview Generate preview
PREVIEW APPROVED User approves Take snapshot
PREVIEW REJECTED User rejects Return to draft
PREVIEW DRAFT User edits Regenerate actions
APPROVED EXECUTING Execute command Begin execution
EXECUTING COMPLETE All actions succeed Cleanup
EXECUTING FAILED Any action fails Log error
FAILED ROLLED_BACK Rollback requested Restore snapshot
FAILED DRAFT User fixes issue Allow retry
ROLLED_BACK DRAFT Ready to retry Clear error state

APPENDIX B: File Structure

ai-agent-simple/
├── task-planner.js          # Phase 1, A1
│   ├── IntentClassifier     # Phase 2, A5
│   ├── ActionGenerator      # Phase 3, A10
│   └── DependencyResolver
├── change-preview.js        # Phase 1, A2
│   ├── DiffGenerator        # Phase 2, A6
│   └── PreviewFormatter     # Phase 3, A11
├── ruvector-bridge.js       # Phase 1, A3
│   ├── SearchAdapter        # Phase 2, A7
│   ├── GraphAdapter         # Phase 2, A8
│   └── RecommendationEngine # Phase 3, A12
├── site-context.js          # Phase 1, A4
│   ├── SiteAnalyzer         # Phase 2, A9
│   └── SchemaDetector       # Phase 3, A13
├── approval-workflow.js     # Phase 4, A14
│   └── WorkflowStateMachine # Phase 4, A15
├── index.js                 # Phase 5, A16 (integration)
└── tests/
    ├── task-planner.test.js # Phase 6, A17
    ├── change-preview.test.js # Phase 6, A18
    ├── ruvector-bridge.test.js # Phase 6, A19
    ├── site-context.test.js # Phase 6, A20
    └── approval-workflow.test.js # Phase 6, A21

END OF GOAP EXECUTION PLAN

This plan provides a complete roadmap from current state to goal state using optimal pathfinding. The plan is ready for execution by the swarm coordinator.