chore: ontology sync + 4 NCL ADRs + landing page update
on+re:
- core.ncl: 5 new Practice nodes (notification-channels,
vapora-capabilities, agent-hot-reload-stable-identity,
merkle-audit-trail, notification-channels) + 5 new edges;
knowledge-graph-execution-history updated with HNSW+BM25+RRF
- state.ncl: production-readiness blocker/catalyst updated (hot-reload
complete, BudgetManager/LLMRouter still require restart);
ontoref-integration catalyst updated (vapora-ontology/reflection
crates, api-catalog.json, nickel contracts)
ADRs (NCL):
- adr-013: KG hybrid search — HNSW+BM25+RRF, rejected in-process scan
- adr-014: capability packages — AgentDefinition→vapora-shared,
DashMap shard-before-await constraint
- adr-015: Merkle audit trail — SHA-256 hash chain, rejected HMAC
- adr-016: agent hot-reload — stable_id=role, learning_profiles survive
drain, BudgetManager excluded from reload scope
landing page:
- 2 new feature boxes: VCS-Agnostic Worktree (jj/git), Ontology Protocol
- KG box: 20→28 tests, HNSW+BM25+RRF description
- Agents box: 71→82 tests, hot-reload + stable_id
- tech stack: Rust 21→23 crates, added jj, Radicle, ontoref badges
- status badge: 620→691 tests
2026-04-07 21:06:48 +01:00
<!-- REVIEW: Status claims "Specification (VAPORA v1.0)" — verify against vapora - agents/src/ which is production - ready at v1.2.0+; code snippets reference docker_image vapora/agents:developer - 0.1.0 which is stale -->
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
# 🤖 Agent Registry & Coordination
## Multi-Agent Orchestration System
**Version**: 0.1.0
**Status**: Specification (VAPORA v1.0 - Multi-Agent)
**Purpose**: Sistema de registro, descubrimiento y coordinación de agentes
---
## 🎯 Objetivo
Crear un **marketplace de agentes** donde:
- ✅ 12 roles especializados trabajan en paralelo
- ✅ Cada agente tiene capacidades, dependencias, versiones claras
- ✅ Discovery & instalación automática
- ✅ Health monitoring + auto-restart
- ✅ Inter-agent communication via NATS JetStream
- ✅ Shared context via MCP/RAG
---
## 📋 Los 12 Roles de Agentes
### Tier 1: Technical Core (Código)
**Architect** (Role ID: `architect` )
- Responsabilidad: Diseño de sistemas, decisiones arquitectónicas
- Entrada: Task de feature compleja, contexto de proyecto
- Salida: ADRs, design documents, architecture diagrams
- LLM óptimo: Claude Opus (complejidad alta)
- Trabajo: Individual o iniciador de workflows
- Canales: Publica decisiones, consulta Decision-Maker
**Developer** (Role ID: `developer` )
- Responsabilidad: Implementación de código
- Entrada: Especificación, ADR, task asignada
- Salida: Código, artifacts, PR
- LLM óptimo: Claude Sonnet (velocidad + calidad)
- Trabajo: Paralelo (múltiples developers por tarea)
- Canales: Escucha de Architect, reporta a Reviewer
**Reviewer** (Role ID: `code-reviewer` )
- Responsabilidad: Revisión de calidad, standards
- Entrada: Pull requests, código propuesto
- Salida: Comments, aprobación/rechazo, sugerencias
- LLM óptimo: Claude Sonnet o Gemini (análisis rápido)
- Trabajo: Paralelo (múltiples reviewers)
- Canales: Escucha PRs de Developer, reporta a Decision-Maker si crítico
**Tester** (Role ID: `tester` )
- Responsabilidad: Testing, benchmarks, QA
- Entrada: Código implementado
- Salida: Test code, benchmark reports, coverage metrics
- LLM óptimo: Claude Sonnet (genera tests)
- Trabajo: Paralelo
- Canales: Escucha de Reviewer, reporta a DevOps
### Tier 2: Documentation & Communication
**Documenter** (Role ID: `documenter` )
- Responsabilidad: Documentación técnica, root files, ADRs
- Entrada: Código, decisions, análisis
- Salida: Docs en `docs/` , actualizaciones README/CHANGELOG
- Usa: Root Files Keeper + doc-lifecycle-manager
- LLM óptimo: GPT-4 (mejor formato)
- Trabajo: Async, actualiza continuamente
- Canales: Escucha cambios en repo, publica docs
**Marketer** (Role ID: `marketer` )
- Responsabilidad: Marketing content, messaging
- Entrada: Nuevas features, releases
- Salida: Blog posts, social content, press releases
- LLM óptimo: Claude Sonnet (creatividad)
- Trabajo: Async
- Canales: Escucha releases, publica content
**Presenter** (Role ID: `presenter` )
- Responsabilidad: Presentaciones, slides, demos
- Entrada: Features, arquitectura, roadmaps
- Salida: Slidev presentations, demo scripts
- LLM óptimo: Claude Sonnet (format + creativity)
- Trabajo: On-demand, por eventos
- Canales: Consulta Architect/Developer
### Tier 3: Operations & Infrastructure
**DevOps** (Role ID: `devops` )
- Responsabilidad: CI/CD, deploys, infrastructure
- Entrada: Code approved, deployment requests
- Salida: Manifests K8s, deployment logs, rollback
- LLM óptimo: Claude Sonnet (IaC)
- Trabajo: Paralelo deploys
- Canales: Escucha de Reviewer (approved), publica deploy logs
**Monitor** (Role ID: `monitor` )
- Responsabilidad: Health checks, alerting, observability
- Entrada: Deployment events, metrics
- Salida: Alerts, dashboards, incident reports
- LLM óptimo: Gemini Flash (análisis rápido)
- Trabajo: Real-time, continuous
- Canales: Publica alerts, escucha todo
**Security** (Role ID: `security` )
- Responsabilidad: Security analysis, compliance, audits
- Entrada: Code changes, PRs, config
- Salida: Security reports, CVE checks, audit logs
- LLM óptimo: Claude Opus (análisis profundo)
- Trabajo: Async, on PRs críticos
- Canales: Escucha de Reviewer, puede bloquear PRs
### Tier 4: Management & Coordination
**ProjectManager** (Role ID: `project-manager` )
- Responsabilidad: Roadmaps, task tracking, coordination
- Entrada: Completed tasks, metrics, blockers
- Salida: Roadmap updates, task assignments, status reports
- LLM óptimo: Claude Sonnet (análisis datos)
- Trabajo: Async, agregador
- Canales: Publica status, escucha completions
**DecisionMaker** (Role ID: `decision-maker` )
- Responsabilidad: Decisiones en conflictos, aprobaciones críticas
- Entrada: Reportes de agentes, decisiones pendientes
- Salida: Aprobaciones, resolución de conflictos
- LLM óptimo: Claude Opus (análisis nuanced)
- Trabajo: On-demand, decisiones críticas
- Canales: Escucha escalaciones, publica decisiones
**Orchestrator** (Role ID: `orchestrator` )
- Responsabilidad: Coordinación de agentes, assignment de tareas
- Entrada: Tasks a hacer, equipo disponible, constraints
- Salida: Task assignments, workflow coordination
- LLM óptimo: Claude Opus (planejamiento)
- Trabajo: Continuous, meta-agent
- Canales: Coordina todo, publica assignments
---
## 🏗️ Agent Registry Structure
### Agent Metadata (SurrealDB)
```rust
pub struct AgentMetadata {
pub id: String, // "architect", "developer-001"
pub role: AgentRole, // Architect, Developer, etc
pub name: String, // "Senior Architect Agent"
pub version: String, // "0.1.0"
pub status: AgentStatus, // Active, Inactive, Updating, Error
pub capabilities: Vec< Capability > , // [Design, ADR, Decisions]
pub skills: Vec< String > , // ["rust", "kubernetes", "distributed-systems"]
pub llm_provider: LLMProvider, // Claude, OpenAI, Gemini, Ollama
pub llm_model: String, // "opus-4"
pub dependencies: Vec< String > , // Agents this one depends on
pub dependents: Vec< String > , // Agents that depend on this one
pub health_check: HealthCheckConfig,
pub max_concurrent_tasks: u32,
pub current_tasks: u32,
pub queue_depth: u32,
pub created_at: DateTime< Utc > ,
pub last_health_check: DateTime< Utc > ,
pub uptime_percentage: f64,
}
pub enum AgentRole {
Architect, Developer, CodeReviewer, Tester,
Documenter, Marketer, Presenter,
DevOps, Monitor, Security,
ProjectManager, DecisionMaker, Orchestrator,
}
pub enum AgentStatus {
Active,
Inactive,
Updating,
Error(String),
Scaling,
}
pub struct Capability {
pub id: String, // "design-adr"
pub name: String, // "Architecture Decision Records"
pub description: String,
pub complexity: Complexity, // Low, Medium, High, Critical
}
pub struct HealthCheckConfig {
pub interval_secs: u32,
pub timeout_secs: u32,
pub consecutive_failures_threshold: u32,
pub auto_restart_enabled: bool,
}
```
### Agent Instance (Runtime)
```rust
pub struct AgentInstance {
pub metadata: AgentMetadata,
pub pod_id: String, // K8s pod ID
pub ip: String,
pub port: u16,
pub start_time: DateTime< Utc > ,
pub last_heartbeat: DateTime< Utc > ,
pub tasks_completed: u32,
pub avg_task_duration_ms: u32,
pub error_count: u32,
pub tokens_used: u64,
pub cost_incurred: f64,
}
```
---
## 📡 Inter-Agent Communication (NATS)
### Message Protocol
```rust
pub enum AgentMessage {
// Task assignment
TaskAssigned {
task_id: String,
agent_id: String,
context: TaskContext,
deadline: DateTime< Utc > ,
},
TaskStarted {
task_id: String,
agent_id: String,
timestamp: DateTime< Utc > ,
},
TaskProgress {
task_id: String,
agent_id: String,
progress_percent: u32,
current_step: String,
},
TaskCompleted {
task_id: String,
agent_id: String,
result: TaskResult,
tokens_used: u64,
duration_ms: u32,
},
TaskFailed {
task_id: String,
agent_id: String,
error: String,
retry_count: u32,
},
// Communication
RequestHelp {
from_agent: String,
to_roles: Vec< AgentRole > ,
context: String,
deadline: DateTime< Utc > ,
},
HelpOffered {
from_agent: String,
to_agent: String,
capability: Capability,
},
ShareContext {
from_agent: String,
to_roles: Vec< AgentRole > ,
context_type: String, // "decision", "analysis", "code"
data: Value,
ttl_minutes: u32,
},
// Coordination
RequestDecision {
from_agent: String,
decision_type: String,
context: String,
options: Vec< String > ,
},
DecisionMade {
decision_id: String,
decision: String,
reasoning: String,
made_by: String,
},
// Health
Heartbeat {
agent_id: String,
status: AgentStatus,
load: f64, // 0.0-1.0
},
}
// NATS Subjects (pub/sub pattern)
pub mod subjects {
pub const TASK_ASSIGNED: & str = "vapora.tasks.assigned"; // Broadcast
pub const TASK_PROGRESS: & str = "vapora.tasks.progress"; // Broadcast
pub const TASK_COMPLETED: & str = "vapora.tasks.completed"; // Broadcast
pub const AGENT_HELP: & str = "vapora.agent.help"; // Request/Reply
pub const AGENT_DECISION: & str = "vapora.agent.decision"; // Request/Reply
pub const AGENT_HEARTBEAT: & str = "vapora.agent.heartbeat"; // Broadcast
}
```
### Pub/Sub Patterns
```rust
// 1. Broadcast: Task assigned to all interested agents
nats.publish("vapora.tasks.assigned", task_message).await?;
// 2. Request/Reply: Developer asks Help from Architect
let help_request = AgentMessage::RequestHelp { ... };
let response = nats.request("vapora.agent.help", help_request, Duration::from_secs(30)).await?;
// 3. Stream: Persist task completion for replay
nats.publish_to_stream("vapora_tasks", "vapora.tasks.completed", completion_message).await?;
// 4. Subscribe: Monitor listens all heartbeats
let mut subscription = nats.subscribe("vapora.agent.heartbeat").await?;
```
---
## 🏪 Agent Discovery & Installation
### Marketplace API
```rust
pub struct AgentRegistry {
pub agents: HashMap< String , AgentMetadata > ,
pub available_agents: HashMap< String , AgentManifest > , // Registry
pub running_agents: HashMap< String , AgentInstance > , // Runtime
}
pub struct AgentManifest {
pub id: String,
pub name: String,
pub version: String,
pub role: AgentRole,
pub docker_image: String, // "vapora/agents:developer-0.1.0"
pub resources: ResourceRequirements,
pub dependencies: Vec< AgentDependency > ,
pub health_check_endpoint: String,
pub capabilities: Vec< Capability > ,
pub documentation: String,
}
pub struct AgentDependency {
pub agent_id: String,
pub role: AgentRole,
pub min_version: String,
pub optional: bool,
}
impl AgentRegistry {
// Discover available agents
pub async fn list_available(& self) -> Vec< AgentManifest > {
self.available_agents.values().cloned().collect()
}
// Install agent
pub async fn install(
& mut self,
manifest: AgentManifest,
count: u32,
) -> anyhow::Result< Vec < AgentInstance > > {
// Check dependencies
for dep in & manifest.dependencies {
if !self.is_available(& dep.agent_id) & & !dep.optional {
return Err(anyhow::anyhow!("Dependency {} required", dep.agent_id));
}
}
// Deploy to K8s (via Provisioning)
let instances = self.deploy_to_k8s(& manifest, count).await?;
// Register
for instance in & instances {
self.running_agents.insert(instance.metadata.id.clone(), instance.clone());
}
Ok(instances)
}
// Health monitoring
pub async fn monitor_health(& mut self) -> anyhow::Result< ()> {
for (id, instance) in & mut self.running_agents {
let health = self.check_agent_health(instance).await?;
if !health.healthy {
if health.consecutive_failures >= instance.metadata.health_check.consecutive_failures_threshold {
if instance.metadata.health_check.auto_restart_enabled {
self.restart_agent(id).await?;
}
}
}
}
Ok(())
}
}
```
---
## 🔄 Shared State & Context
### Context Management
```rust
pub struct SharedContext {
pub project_id: String,
pub active_tasks: HashMap< String , Task > ,
pub agent_states: HashMap< String , AgentState > ,
pub decisions: HashMap< String , Decision > ,
pub shared_knowledge: HashMap< String , Value > , // RAG indexed
}
pub struct AgentState {
pub agent_id: String,
pub current_task: Option< String > ,
pub last_action: DateTime< Utc > ,
pub available_until: DateTime< Utc > ,
pub context_from_previous_tasks: Vec< String > ,
}
// Access via MCP
impl SharedContext {
pub async fn get_context(& self, agent_id: & str) -> anyhow::Result< AgentState > {
self.agent_states.get(agent_id)
.cloned()
.ok_or(anyhow::anyhow!("Agent {} not found", agent_id))
}
pub async fn share_decision(& mut self, decision: Decision) -> anyhow::Result< ()> {
self.decisions.insert(decision.id.clone(), decision);
// Notify interested agents via NATS
Ok(())
}
pub async fn share_knowledge(& mut self, key: String, value: Value) -> anyhow::Result< ()> {
self.shared_knowledge.insert(key, value);
// Index in RAG
Ok(())
}
}
```
---
## 🎯 Implementation Checklist
- [ ] Define AgentMetadata + AgentInstance structs
- [ ] NATS JetStream integration
- [ ] Agent Registry CRUD operations
- [ ] Health monitoring + auto-restart logic
- [ ] Agent marketplace UI (Leptos)
- [ ] Installation flow (manifest parsing, K8s deployment)
- [ ] Pub/Sub message handlers
- [ ] Request/Reply pattern implementation
- [ ] Shared context via MCP
- [ ] CLI: `vapora agent list` , `vapora agent install` , `vapora agent scale`
- [ ] Logging + monitoring (Prometheus metrics)
- [ ] Tests (mocking, integration)
---
## 📊 Success Metrics
✅ Agents register and appear in registry
✅ Health checks run every N seconds
✅ Unhealthy agents restart automatically
✅ NATS messages route correctly
✅ Shared context accessible to all agents
✅ Agent scaling works (1 → N replicas)
✅ Task assignment < 100ms latency
---
**Version**: 0.1.0
**Status**: ✅ Specification Complete (VAPORA v1.0)
**Purpose**: Multi-agent registry and coordination system