Implement intelligent agent learning from Knowledge Graph execution history with per-task-type expertise tracking, recency bias, and learning curves. ## Phase 5.3 Implementation ### Learning Infrastructure (✅ Complete) - LearningProfileService with per-task-type expertise metrics - TaskTypeExpertise model tracking success_rate, confidence, learning curves - Recency bias weighting: recent 7 days weighted 3x higher (exponential decay) - Confidence scoring prevents overfitting: min(1.0, executions / 20) - Learning curves computed from daily execution windows ### Agent Scoring Service (✅ Complete) - Unified AgentScore combining SwarmCoordinator + learning profiles - Scoring formula: 0.3*base + 0.5*expertise + 0.2*confidence - Rank agents by combined score for intelligent assignment - Support for recency-biased scoring (recent_success_rate) - Methods: rank_agents, select_best, rank_agents_with_recency ### KG Integration (✅ Complete) - KGPersistence::get_executions_for_task_type() - query by agent + task type - KGPersistence::get_agent_executions() - all executions for agent - Coordinator::load_learning_profile_from_kg() - core KG→Learning integration - Coordinator::load_all_learning_profiles() - batch load for multiple agents - Convert PersistedExecution → ExecutionData for learning calculations ### Agent Assignment Integration (✅ Complete) - AgentCoordinator uses learning profiles for task assignment - extract_task_type() infers task type from title/description - assign_task() scores candidates using AgentScoringService - Fallback to load-based selection if no learning data available - Learning profiles stored in coordinator.learning_profiles RwLock ### Profile Adapter Enhancements (✅ Complete) - create_learning_profile() - initialize empty profiles - add_task_type_expertise() - set task-type expertise - update_profile_with_learning() - update swarm profiles from learning ## Files Modified ### vapora-knowledge-graph/src/persistence.rs (+30 lines) - get_executions_for_task_type(agent_id, task_type, limit) - get_agent_executions(agent_id, limit) ### vapora-agents/src/coordinator.rs (+100 lines) - load_learning_profile_from_kg() - core KG integration method - load_all_learning_profiles() - batch loading for agents - assign_task() already uses learning-based scoring via AgentScoringService ### Existing Complete Implementation - vapora-knowledge-graph/src/learning.rs - calculation functions - vapora-agents/src/learning_profile.rs - data structures and expertise - vapora-agents/src/scoring.rs - unified scoring service - vapora-agents/src/profile_adapter.rs - adapter methods ## Tests Passing - learning_profile: 7 tests ✅ - scoring: 5 tests ✅ - profile_adapter: 6 tests ✅ - coordinator: learning-specific tests ✅ ## Data Flow 1. Task arrives → AgentCoordinator::assign_task() 2. Extract task_type from description 3. Query KG for task-type executions (load_learning_profile_from_kg) 4. Calculate expertise with recency bias 5. Score candidates (SwarmCoordinator + learning) 6. Assign to top-scored agent 7. Execution result → KG → Update learning profiles ## Key Design Decisions ✅ Recency bias: 7-day half-life with 3x weight for recent performance ✅ Confidence scoring: min(1.0, total_executions / 20) prevents overfitting ✅ Hierarchical scoring: 30% base load, 50% expertise, 20% confidence ✅ KG query limit: 100 recent executions per task-type for performance ✅ Async loading: load_learning_profile_from_kg supports concurrent loads ## Next: Phase 5.4 - Cost Optimization Ready to implement budget enforcement and cost-aware provider selection.
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🔄 Multi-Agent Workflows
End-to-End Parallel Task Orchestration
Version: 0.1.0 Status: Specification (VAPORA v1.0 - Workflows) Purpose: Workflows where 10+ agents work in parallel, coordinated automatically
🎯 Objetivo
Orquestar workflows donde múltiples agentes trabajan en paralelo en diferentes aspectos de una tarea, sin intervención manual:
Feature Request
↓
ProjectManager crea task
↓ (paralelo)
Architect diseña ────────┐
Developer implementa ────├─→ Reviewer revisa ──┐
Tester escribe tests ────┤ ├─→ DecisionMaker aprueba
Documenter prepara docs ─┤ ├─→ DevOps deploya
Security audita ────────┘ │
↓
Marketer promociona
📋 Workflow: Feature Compleja End-to-End
Fase 1: Planificación (Serial - Requiere aprobación)
Agentes: Architect, ProjectManager, DecisionMaker
Timeline: 1-2 horas
Workflow: feature-auth-mfa
Status: planning
Created: 2025-11-09T10:00:00Z
Steps:
1_architect_designs:
agent: architect
input: feature_request, project_context
task_type: ArchitectureDesign
quality: Critical
estimated_duration: 45min
output:
- design_doc.md
- adr-001-mfa-strategy.md
- architecture_diagram.svg
2_pm_validates:
dependencies: [1_architect_designs]
agent: project-manager
task_type: GeneralQuery
input: design_doc, project_timeline
action: validate_feasibility
3_decision_maker_approves:
dependencies: [2_pm_validates]
agent: decision-maker
task_type: GeneralQuery
input: design, feasibility_report
approval_required: true
escalation_if: ["too risky", "breaks roadmap"]
Output: ADR aprobado, design doc, go/no-go decision
Fase 2: Implementación (Paralelo - Máxima concurrencia)
Agentes: Developer (×3), Tester, Security, Documenter (async)
Timeline: 3-5 días
4_frontend_dev:
dependencies: [3_decision_maker_approves]
agent: developer-frontend
skill_match: frontend
input: design_doc, api_spec
tasks:
- implement_mfa_ui
- add_totp_input
- add_webauthn_button
parallel_with: [4_backend_dev, 5_security_setup, 6_docs_start]
max_duration: 4days
4_backend_dev:
dependencies: [3_decision_maker_approves]
agent: developer-backend
skill_match: backend, security
input: design_doc, database_schema
tasks:
- implement_mfa_service
- add_totp_verification
- add_webauthn_endpoint
parallel_with: [4_frontend_dev, 5_security_setup, 6_docs_start]
max_duration: 4days
5_security_audit:
dependencies: [3_decision_maker_approves]
agent: security
input: design_doc, threat_model
tasks:
- threat_modeling
- security_review
- vulnerability_scan_plan
parallel_with: [4_frontend_dev, 4_backend_dev, 6_docs_start]
can_block_deployment: true
6_docs_start:
dependencies: [3_decision_maker_approves]
agent: documenter
input: design_doc
tasks:
- create_adr_doc
- start_implementation_guide
parallel_with: [4_frontend_dev, 4_backend_dev, 5_security_audit]
low_priority: true
Status: in_progress
Parallel_agents: 5
Progress: 60%
Blockers: none
Output:
- Frontend implementation + PRs
- Backend implementation + PRs
- Security audit report
- Initial documentation
Fase 3: Código Review (Paralelo pero gated)
Agentes: CodeReviewer (×2), Security, Tester
Timeline: 1-2 días
7a_frontend_review:
dependencies: [4_frontend_dev]
agent: code-reviewer-frontend
input: frontend_pr
actions: [comment, request_changes, approve]
must_pass: 1 # At least 1 reviewer
can_block_merge: true
7b_backend_review:
dependencies: [4_backend_dev]
agent: code-reviewer-backend
input: backend_pr
actions: [comment, request_changes, approve]
must_pass: 1
security_required: true # Security must also approve
7c_security_review:
dependencies: [4_backend_dev, 5_security_audit]
agent: security
input: backend_pr, security_audit
actions: [scan, approve_or_block]
critical_vulns_block_merge: true
high_vulns_require_mitigation: true
7d_test_coverage:
dependencies: [4_frontend_dev, 4_backend_dev]
agent: tester
input: frontend_pr, backend_pr
actions: [run_tests, check_coverage, benchmark]
must_pass: tests_passing && coverage > 85%
Status: in_progress
Parallel_reviewers: 4
Approved: frontend_review
Pending: backend_review (awaiting security_review)
Blockers: security_review
Output:
- Approved PRs (if all pass)
- Comments & requested changes
- Test coverage report
- Security clearance
Fase 4: Merge & Deploy (Serial - Ordered)
Agentes: CodeReviewer, DevOps, Monitor
Timeline: 1-2 horas
8_merge_to_dev:
dependencies: [7a_frontend_review, 7b_backend_review, 7c_security_review, 7d_test_coverage]
agent: code-reviewer
action: merge_to_dev
requires: all_approved
9_deploy_staging:
dependencies: [8_merge_to_dev]
agent: devops
environment: staging
actions: [trigger_ci, deploy_manifests, smoke_test]
automatic_after_merge: true
timeout: 30min
10_smoke_test:
dependencies: [9_deploy_staging]
agent: tester
test_type: smoke
environments: [staging]
must_pass: all
11_monitor_staging:
dependencies: [9_deploy_staging]
agent: monitor
duration: 1hour
metrics: [error_rate, latency, cpu, memory]
alert_if: error_rate > 1% or p99_latency > 500ms
Status: in_progress
Completed: 8_merge_to_dev
In_progress: 9_deploy_staging (20min elapsed)
Pending: 10_smoke_test, 11_monitor_staging
Output:
- Code merged to dev
- Deployed to staging
- Smoke tests pass
- Monitoring active
Fase 5: Final Validation & Release
Agentes: DecisionMaker, DevOps, Marketer, Monitor
Timeline: 1-3 horas
12_final_approval:
dependencies: [10_smoke_test, 11_monitor_staging]
agent: decision-maker
input: test_results, monitoring_report, security_clearance
action: approve_for_production
if_blocked: defer_to_next_week
13_deploy_production:
dependencies: [12_final_approval]
agent: devops
environment: production
deployment_strategy: blue_green # 0 downtime
actions: [deploy, health_check, traffic_switch]
rollback_on: any_error
14_monitor_production:
dependencies: [13_deploy_production]
agent: monitor
duration: 24hours
alert_thresholds: [error_rate > 0.5%, p99 > 300ms, cpu > 80%]
auto_rollback_if: critical_error
15_announce_release:
dependencies: [13_deploy_production] # Can start once deployed
agent: marketer
async: true
actions: [draft_blog_post, announce_on_twitter, create_demo_video]
16_update_docs:
dependencies: [13_deploy_production]
agent: documenter
async: true
actions: [update_changelog, publish_guide, update_roadmap]
Status: completed
Deployed: 2025-11-10T14:00:00Z
Monitoring: Active
Release_notes: docs/releases/v1.2.0.md
Output:
- Deployed to production
- 24h monitoring active
- Blog post + social media
- Docs updated
- Release notes published
🔄 Workflow State Machine
Created
↓
Planning (serial, approval-gated)
├─ Architect designs
├─ PM validates
└─ DecisionMaker approves → GO / NO-GO
↓
Implementation (parallel)
├─ Frontend dev
├─ Backend dev
├─ Security audit
├─ Tester setup
└─ Documenter start
↓
Review (parallel but gated)
├─ Code review
├─ Security review
├─ Test execution
└─ Coverage check
↓
Merge & Deploy (serial, ordered)
├─ Merge to dev
├─ Deploy staging
├─ Smoke test
└─ Monitor staging
↓
Release (parallel async)
├─ Final approval
├─ Deploy production
├─ Monitor 24h
├─ Marketing announce
└─ Docs update
↓
Completed / Rolled back
Transitions:
- Blocked → can escalate to DecisionMaker
- Failed → auto-rollback if production
- Waiting → timeout after N hours
🎯 Workflow DSL (YAML/TOML)
Minimal Example
workflow:
id: feature-auth
title: Implement MFA
agents:
architect:
role: Architect
parallel_with: [pm]
pm:
role: ProjectManager
depends_on: [architect]
developer:
role: Developer
depends_on: [pm]
parallelizable: true
approval_required_at: [architecture, deploy_production]
allow_concurrent_agents: 10
timeline_hours: 48
Complex Example (Feature-complete)
workflow:
id: feature-user-preferences
title: User Preferences System
created_at: 2025-11-09T10:00:00Z
phases:
phase_1_design:
duration_hours: 2
serial: true
steps:
- name: architect_designs
agent: architect
input: feature_spec
output: design_doc
- name: architect_creates_adr
agent: architect
depends_on: architect_designs
output: adr-017.md
- name: pm_reviews
agent: project-manager
depends_on: architect_creates_adr
approval_required: true
phase_2_implementation:
duration_hours: 48
parallel: true
max_concurrent_agents: 6
steps:
- name: frontend_dev
agent: developer
skill_match: frontend
depends_on: [architect_designs]
- name: backend_dev
agent: developer
skill_match: backend
depends_on: [architect_designs]
- name: db_migration
agent: devops
depends_on: [architect_designs]
- name: security_review
agent: security
depends_on: [architect_designs]
- name: docs_start
agent: documenter
depends_on: [architect_creates_adr]
priority: low
phase_3_review:
duration_hours: 16
gate: all_tests_pass && all_reviews_approved
steps:
- name: frontend_review
agent: code-reviewer
depends_on: frontend_dev
- name: backend_review
agent: code-reviewer
depends_on: backend_dev
- name: tests
agent: tester
depends_on: [frontend_dev, backend_dev]
- name: deploy_staging
agent: devops
depends_on: [frontend_review, backend_review, tests]
phase_4_release:
duration_hours: 4
steps:
- name: final_approval
agent: decision-maker
depends_on: phase_3_review
- name: deploy_production
agent: devops
depends_on: final_approval
strategy: blue_green
- name: announce
agent: marketer
depends_on: deploy_production
async: true
🔧 Runtime: Monitoring & Adjustment
Dashboard (Real-Time)
Workflow: feature-auth-mfa
Status: in_progress (Phase 2/5)
Progress: 45%
Timeline: 2/4 days remaining
Active Agents (5/12):
├─ architect-001 🟢 Designing (80% done)
├─ developer-frontend-001 🟢 Implementing (60% done)
├─ developer-backend-001 🟢 Implementing (50% done)
├─ security-001 🟢 Auditing (70% done)
└─ documenter-001 🟡 Waiting for PR links
Pending Agents (4):
├─ code-reviewer-001 ⏳ Waiting for frontend_dev
├─ code-reviewer-002 ⏳ Waiting for backend_dev
├─ tester-001 ⏳ Waiting for dev completion
└─ devops-001 ⏳ Waiting for reviews
Blockers: none
Issues: none
Risks: none
Timeline Projection:
- Design: ✅ 2h (completed)
- Implementation: 3d (50% done, on track)
- Review: 1d (scheduled)
- Deploy: 4h (scheduled)
Total ETA: 4d (vs 5d planned, 1d early!)
Workflow Adjustments
pub enum WorkflowAdjustment {
// Add more agents if progress slow
AddAgent { agent_role: AgentRole, count: u32 },
// Parallelize steps that were serial
Parallelize { step_ids: Vec<String> },
// Skip optional steps to save time
SkipOptionalSteps { step_ids: Vec<String> },
// Escalate blocker to DecisionMaker
EscalateBlocker { step_id: String },
// Pause workflow for manual review
Pause { reason: String },
// Cancel workflow if infeasible
Cancel { reason: String },
}
// Example: If timeline too tight, add agents
if projected_timeline > planned_timeline {
workflow.adjust(WorkflowAdjustment::AddAgent {
agent_role: AgentRole::Developer,
count: 2,
}).await?;
}
🎯 Implementation Checklist
- Workflow YAML/TOML parser
- State machine executor (Created→Completed)
- Parallel task scheduler
- Dependency resolution (topological sort)
- Gate evaluation (all_passed, any_approved, etc.)
- Blocking & escalation logic
- Rollback on failure
- Real-time dashboard
- Audit trail (who did what, when, why)
- CLI:
vapora workflow run feature-auth.yaml - CLI:
vapora workflow status --id feature-auth - Monitoring & alerting
📊 Success Metrics
✅ 10+ agents coordinated without errors ✅ Parallel execution actual (not serial) ✅ Dependencies respected ✅ Approval gates enforce correctly ✅ Rollback works on failure ✅ Dashboard updates real-time ✅ Workflow completes in <5% over estimated time
Version: 0.1.0 Status: ✅ Specification Complete (VAPORA v1.0) Purpose: Multi-agent parallel workflow orchestration