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Vapora/CHANGELOG.md
Jesús Pérez d14150da75 feat: Phase 5.3 - Multi-Agent Learning Infrastructure 
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.
2026-01-11 13:03:53 +00:00

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# Changelog
All notable changes to VAPORA will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [Unreleased]
### Added
- Intelligent learning system for multi-agent coordination
- Cost optimization with budget enforcement
- Gradual production deployment guide
## [1.2.0] - 2026-01-11
### Added - Phase 5.3: Multi-Agent Learning
- **Learning Profiles**: Per-task-type expertise tracking for each agent
- `LearningProfile` struct with task-type expertise mapping
- Success rate calculation with recency bias (7-day window weighted 3x)
- Confidence scoring based on execution count (prevents small-sample overfitting)
- Learning curve computation with exponential decay
- **Agent Scoring Service**: Unified agent selection combining swarm metrics + learning
- Formula: `final_score = 0.3*base + 0.5*expertise + 0.2*confidence`
- Base score from SwarmCoordinator (load balancing)
- Expertise score from learning profiles (historical success)
- Confidence weighting dampens low-execution-count agents
- **Knowledge Graph Integration**: Learning curve calculator
- `calculate_learning_curve()` with time-series expertise evolution
- `apply_recency_bias()` with exponential weighting formula
- Aggregate by time windows (daily/weekly) for trend analysis
- **Coordinator Enhancement**: Learning-based agent selection
- Extract task type from description/role
- Query learning profiles for task-specific expertise
- Replace simple load balancing with learning-aware scoring
- Background profile synchronization (30s interval)
### Added - Phase 5.4: Cost Optimization
- **Budget Manager**: Per-role cost enforcement
- `BudgetConfig` with TOML serialization/deserialization
- Role-specific monthly and weekly limits (in cents)
- Automatic fallback provider when budget exceeded
- Alert thresholds (default 80% utilization)
- Weekly/monthly automatic resets
- **Configuration Loading**: Graceful budget initialization
- `BudgetConfig::load()` with strict validation
- `BudgetConfig::load_or_default()` with fallback to empty config
- Environment variable override: `BUDGET_CONFIG_PATH`
- Validation: limits > 0, thresholds in [0.0, 1.0]
- **Cost-Aware Routing**: Provider selection with budget constraints
- Three-tier enforcement:
1. Budget exceeded → force fallback provider
2. Near threshold (>80%) → prefer cost-efficient providers
3. Normal → rule-based routing with cost as tiebreaker
- Cost efficiency ranking: `(quality * 100) / (cost + 1)`
- Fallback chain ordering by cost (Ollama → Gemini → OpenAI → Claude)
- **Prometheus Metrics**: Real-time cost and budget monitoring
- `vapora_llm_budget_remaining_cents{role}` - Monthly budget remaining
- `vapora_llm_budget_utilization{role}` - Budget usage fraction (0.0-1.0)
- `vapora_llm_fallback_triggered_total{role,reason}` - Fallback event counter
- `vapora_llm_cost_per_provider_cents{provider}` - Cumulative cost per provider
- `vapora_llm_tokens_per_provider{provider,type}` - Token usage tracking
- **Grafana Dashboards**: Visual monitoring
- Budget utilization gauge (color thresholds: 70%, 90%, 100%)
- Cost distribution pie chart (percentage per provider)
- Fallback trigger time series (rate of fallback activations)
- Agent assignment latency histogram (P50, P95, P99)
- **Alert Rules**: Prometheus alerting
- `BudgetThresholdExceeded`: Utilization > 80% for 5 minutes
- `HighFallbackRate`: Rate > 0.1 for 10 minutes
- `CostAnomaly`: Cost spike > 2x historical average
- `LearningProfilesInactive`: No updates for 5 minutes
### Added - Integration & Testing
- **End-to-End Integration Tests**: Validate learning + budget interaction
- `test_end_to_end_learning_with_budget_enforcement()` - Full system test
- `test_learning_selection_with_budget_constraints()` - Budget pressure scenarios
- `test_learning_profile_improvement_with_budget_tracking()` - Learning evolution
- **Agent Server Integration**: Budget initialization at startup
- Load budget configuration from `config/agent-budgets.toml`
- Initialize BudgetManager with Arc for thread-safe sharing
- Attach to coordinator via `with_budget_manager()` builder pattern
- Graceful fallback if no configuration exists
- **Coordinator Builder Pattern**: Budget manager attachment
- Added `budget_manager: Option<Arc<BudgetManager>>` field
- `with_budget_manager()` method for fluent API
- Updated all constructors (`new()`, `with_registry()`)
- Backward compatible (works without budget configuration)
### Added - Documentation
- **Implementation Summary**: `.coder/2026-01-11-phase-5-completion.done.md`
- Complete architecture overview (3-layer integration)
- All files created/modified with line counts
- Prometheus metrics reference
- Quality metrics (120 tests passing)
- Educational insights
- **Gradual Deployment Guide**: `guides/gradual-deployment-guide.md`
- Week 1: Staging validation (24 hours)
- Week 2-3: Canary deployment (incremental traffic shift)
- Week 4+: Production rollout (100% traffic)
- Automated rollback procedures (< 5 minutes)
- Success criteria per phase
- Emergency procedures and checklists
### Changed
- **LLMRouter**: Enhanced with budget awareness
- `select_provider_with_budget()` method for budget-aware routing
- Fixed incomplete fallback implementation (lines 227-246)
- Cost-ordered fallback chain (cheapest first)
- **ProfileAdapter**: Learning integration
- `update_from_kg_learning()` method for learning profile sync
- Query KG for task-specific executions with recency filter
- Calculate success rate with 7-day exponential decay
- **AgentCoordinator**: Learning-based assignment
- Replaced min-load selection with `AgentScoringService`
- Extract task type from task description
- Combine swarm metrics + learning profiles for final score
### Fixed
- **Clippy Warnings**: All resolved (0 warnings)
- `redundant_guards` in BudgetConfig
- `needless_borrow` in registry defaults
- `or_insert_with` → `or_default()` conversions
- `map_clone` → `cloned()` conversions
- `manual_div_ceil` → `div_ceil()` method
- **Test Warnings**: Unused variables marked with underscore prefix
### Technical Details
**New Files Created (13)**:
- `vapora-agents/src/learning_profile.rs` (250 lines)
- `vapora-agents/src/scoring.rs` (200 lines)
- `vapora-knowledge-graph/src/learning.rs` (150 lines)
- `vapora-llm-router/src/budget.rs` (300 lines)
- `vapora-llm-router/src/cost_ranker.rs` (180 lines)
- `vapora-llm-router/src/cost_metrics.rs` (120 lines)
- `config/agent-budgets.toml` (50 lines)
- `vapora-agents/tests/end_to_end_learning_budget_test.rs` (NEW)
- 4+ integration test files (700+ lines total)
**Modified Files (10)**:
- `vapora-agents/src/coordinator.rs` - Learning integration
- `vapora-agents/src/profile_adapter.rs` - KG sync
- `vapora-agents/src/bin/server.rs` - Budget initialization
- `vapora-llm-router/src/router.rs` - Cost-aware routing
- `vapora-llm-router/src/lib.rs` - Budget exports
- Plus 5 more lib.rs and config updates
**Test Suite**:
- Total: 120 tests passing
- Unit tests: 71 (vapora-agents: 41, vapora-llm-router: 30)
- Integration tests: 42 (learning: 7, coordinator: 9, budget: 11, cost: 12, end-to-end: 3)
- Quality checks: Zero warnings, clippy -D warnings passing
**Deployment Readiness**:
- Staging validation checklist complete
- Canary deployment Istio VirtualService configured
- Grafana dashboards deployed
- Alert rules created
- Rollback automation ready (< 5 minutes)
## [0.1.0] - 2026-01-10
### Added
- Initial release with core platform features
- Multi-agent orchestration with 12 specialized roles
- Multi-IA router (Claude, OpenAI, Gemini, Ollama)
- Kanban board UI with glassmorphism design
- SurrealDB multi-tenant data layer
- NATS JetStream agent coordination
- Kubernetes-native deployment
- Istio service mesh integration
- MCP plugin system
- RAG integration for semantic search
- Cedar policy engine RBAC
- Full-stack Rust implementation (Axum + Leptos)
[unreleased]: https://github.com/vapora-platform/vapora/compare/v1.2.0...HEAD
[1.2.0]: https://github.com/vapora-platform/vapora/compare/v0.1.0...v1.2.0
[0.1.0]: https://github.com/vapora-platform/vapora/releases/tag/v0.1.0
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