Vapora/docs/integrations/provisioning-integration.md

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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
# ⚙️ Provisioning Integration
## Deploying VAPORA via Provisioning Taskservs & KCL
**Version**: 0.1.0
**Status**: Specification (VAPORA v1.0 Deployment)
**Purpose**: How Provisioning creates and manages VAPORA infrastructure
---
## 🎯 Objetivo
Provisioning es el **deployment engine** para VAPORA:
- Define infraestructura con **KCL schemas** (no Helm)
- Crea **taskservs** para cada componente VAPORA
- Ejecuta **batch workflows** para operaciones complejas
- Escala **agents** dinámicamente
- Monitorea **health** y triggers **rollback**
---
## 📁 VAPORA Workspace Structure
```
provisioning/vapora-wrksp/
├── workspace.toml # Workspace definition
├── kcl/ # KCL Infrastructure-as-Code
│ ├── cluster.k # K8s cluster (nodes, networks)
│ ├── services.k # Microservices (backend, agents)
│ ├── storage.k # SurrealDB + Rook Ceph
│ ├── agents.k # Agent pools + scaling
│ └── multi-ia.k # LLM Router + providers
├── taskservs/ # Taskserv definitions
│ ├── vapora-backend.toml # API backend
│ ├── vapora-frontend.toml # Web UI
│ ├── vapora-agents.toml # Agent runtime
│ ├── vapora-mcp-gateway.toml # MCP plugins
│ └── vapora-llm-router.toml # Multi-IA router
├── workflows/ # Batch operations
│ ├── deploy-full-stack.yaml
│ ├── scale-agents.yaml
│ ├── upgrade-vapora.yaml
│ └── disaster-recovery.yaml
└── README.md # Setup guide
```
---
## 🏗️ KCL Schemas
### 1. Cluster Definition (cluster.k)
```kcl
import kcl_plugin.kubernetes as k
# VAPORA Cluster
cluster = k.Cluster {
name = "vapora-cluster"
version = "1.30"
network = {
cni = "cilium" # Network plugin
serviceMesh = "istio" # Service mesh
ingressController = "istio-gateway"
}
storage = {
provider = "rook-ceph"
replication_factor = 3
storage_classes = [
{ name = "ssd", type = "nvme" },
{ name = "hdd", type = "sata" },
]
}
nodes = [
# Control plane
{
role = "control-plane"
count = 3
instance_type = "t3.medium"
resources = { cpu = "2", memory = "4Gi" }
},
# Worker nodes for agents (scalable)
{
role = "worker"
count = 5
instance_type = "t3.large"
resources = { cpu = "4", memory = "8Gi" }
labels = { workload = "agents", tier = "compute" }
taints = []
},
# Worker nodes for data
{
role = "worker"
count = 3
instance_type = "t3.xlarge"
resources = { cpu = "8", memory = "16Gi" }
labels = { workload = "data", tier = "storage" }
},
]
addons = [
"metrics-server",
"prometheus",
"grafana",
]
}
```
### 2. Services Definition (services.k)
```kcl
import kcl_plugin.kubernetes as k
services = [
# Backend API
{
name = "vapora-backend"
namespace = "vapora-system"
replicas = 3
image = "vapora/backend:0.1.0"
port = 8080
resources = {
requests = { cpu = "1", memory = "2Gi" }
limits = { cpu = "2", memory = "4Gi" }
}
env = [
{ name = "DATABASE_URL", value = "surrealdb://surreal-0.vapora-system:8000" },
{ name = "NATS_URL", value = "nats://nats-0.vapora-system:4222" },
]
},
# Frontend
{
name = "vapora-frontend"
namespace = "vapora-system"
replicas = 2
image = "vapora/frontend:0.1.0"
port = 3000
resources = {
requests = { cpu = "500m", memory = "512Mi" }
limits = { cpu = "1", memory = "1Gi" }
}
},
# Agent Runtime
{
name = "vapora-agents"
namespace = "vapora-agents"
replicas = 3
image = "vapora/agents:0.1.0"
port = 8089
resources = {
requests = { cpu = "2", memory = "4Gi" }
limits = { cpu = "4", memory = "8Gi" }
}
# Autoscaling
hpa = {
min_replicas = 3
max_replicas = 20
target_cpu = "70"
}
},
# MCP Gateway
{
name = "vapora-mcp-gateway"
namespace = "vapora-system"
replicas = 2
image = "vapora/mcp-gateway:0.1.0"
port = 8888
},
# LLM Router
{
name = "vapora-llm-router"
namespace = "vapora-system"
replicas = 2
image = "vapora/llm-router:0.1.0"
port = 8899
env = [
{ name = "CLAUDE_API_KEY", valueFrom = "secret:vapora-secrets:claude-key" },
{ name = "OPENAI_API_KEY", valueFrom = "secret:vapora-secrets:openai-key" },
{ name = "GEMINI_API_KEY", valueFrom = "secret:vapora-secrets:gemini-key" },
]
},
]
```
### 3. Storage Definition (storage.k)
```kcl
import kcl_plugin.kubernetes as k
storage = {
# SurrealDB StatefulSet
surrealdb = {
name = "surrealdb"
namespace = "vapora-system"
replicas = 3
image = "surrealdb/surrealdb:1.8"
port = 8000
storage = {
size = "50Gi"
storage_class = "rook-ceph"
}
},
# Redis cache
redis = {
name = "redis"
namespace = "vapora-system"
replicas = 1
image = "redis:7-alpine"
port = 6379
storage = {
size = "20Gi"
storage_class = "ssd"
}
},
# NATS JetStream
nats = {
name = "nats"
namespace = "vapora-system"
replicas = 3
image = "nats:2.10-scratch"
port = 4222
storage = {
size = "30Gi"
storage_class = "rook-ceph"
}
},
}
```
### 4. Agent Pools (agents.k)
```kcl
agents = {
architect = {
role_id = "architect"
replicas = 2
max_concurrent = 1
container = {
image = "vapora/agents:architect-0.1.0"
resources = { cpu = "4", memory = "8Gi" }
}
},
developer = {
role_id = "developer"
replicas = 5 # Can scale to 20
max_concurrent = 2
container = {
image = "vapora/agents:developer-0.1.0"
resources = { cpu = "4", memory = "8Gi" }
}
hpa = {
min_replicas = 5
max_replicas = 20
target_queue_depth = 10 # Scale when queue > 10
}
},
reviewer = {
role_id = "code-reviewer"
replicas = 3
max_concurrent = 2
container = {
image = "vapora/agents:reviewer-0.1.0"
resources = { cpu = "2", memory = "4Gi" }
}
},
# ... other 9 roles
}
```
---
## 🛠️ Taskservs Definition
### Example: Backend Taskserv
```toml
# taskservs/vapora-backend.toml
[taskserv]
name = "vapora-backend"
type = "service"
version = "0.1.0"
description = "VAPORA REST API backend"
[source]
repository = "ssh://git@repo.jesusperez.pro:32225/jesus/Vapora.git"
branch = "main"
path = "vapora-backend/"
[build]
runtime = "rust"
build_command = "cargo build --release"
binary_path = "target/release/vapora-backend"
dockerfile = "Dockerfile.backend"
[deployment]
namespace = "vapora-system"
replicas = 3
image = "vapora/backend:${version}"
image_pull_policy = "Always"
[ports]
http = 8080
metrics = 9090
[resources]
requests = { cpu = "1000m", memory = "2Gi" }
limits = { cpu = "2000m", memory = "4Gi" }
[health_check]
path = "/health"
interval_secs = 10
timeout_secs = 5
failure_threshold = 3
[dependencies]
- "surrealdb" # Must exist
- "nats" # Must exist
- "redis" # Optional
[scaling]
min_replicas = 3
max_replicas = 10
target_cpu_percent = 70
target_memory_percent = 80
[environment]
DATABASE_URL = "surrealdb://surrealdb-0:8000"
NATS_URL = "nats://nats-0:4222"
REDIS_URL = "redis://redis-0:6379"
RUST_LOG = "debug,vapora=trace"
[secrets]
JWT_SECRET = "secret:vapora-secrets:jwt-secret"
DATABASE_PASSWORD = "secret:vapora-secrets:db-password"
```
---
## 🔄 Workflows (Batch Operations)
### Deploy Full Stack
```yaml
# workflows/deploy-full-stack.yaml
apiVersion: provisioning/v1
kind: Workflow
metadata:
name: deploy-vapora-full-stack
namespace: vapora-system
spec:
description: "Deploy complete VAPORA stack from scratch"
steps:
# Step 1: Create cluster
- name: create-cluster
task: provisioning.cluster
params:
config: kcl/cluster.k
timeout: 1h
on_failure: abort
# Step 2: Install operators (Istio, Prometheus, Rook)
- name: install-addons
task: provisioning.addon
depends_on: [create-cluster]
params:
addons: [istio, prometheus, rook-ceph]
timeout: 30m
# Step 3: Deploy data layer
- name: deploy-data
task: provisioning.deploy-taskservs
depends_on: [install-addons]
params:
taskservs: [surrealdb, redis, nats]
timeout: 30m
# Step 4: Deploy core services
- name: deploy-core
task: provisioning.deploy-taskservs
depends_on: [deploy-data]
params:
taskservs: [vapora-backend, vapora-llm-router, vapora-mcp-gateway]
timeout: 30m
# Step 5: Deploy frontend
- name: deploy-frontend
task: provisioning.deploy-taskservs
depends_on: [deploy-core]
params:
taskservs: [vapora-frontend]
timeout: 15m
# Step 6: Deploy agent pools
- name: deploy-agents
task: provisioning.deploy-agents
depends_on: [deploy-core]
params:
agents: [architect, developer, reviewer, tester, documenter, devops, monitor, security, pm, decision-maker, orchestrator, presenter]
initial_replicas: { architect: 2, developer: 5, ... }
timeout: 30m
# Step 7: Verify health
- name: health-check
task: provisioning.health-check
depends_on: [deploy-agents, deploy-frontend]
params:
services: all
timeout: 5m
on_failure: rollback
# Step 8: Initialize database
- name: init-database
task: provisioning.run-migrations
depends_on: [health-check]
params:
sql_files: [migrations/*.surql]
timeout: 10m
# Step 9: Configure ingress
- name: configure-ingress
task: provisioning.configure-ingress
depends_on: [init-database]
params:
gateway: istio-gateway
hosts:
- vapora.example.com
timeout: 10m
rollback_on_failure: true
on_completion:
- name: notify-slack
task: notifications.slack
params:
webhook: "${SLACK_WEBHOOK}"
message: "VAPORA deployment completed successfully!"
```
### Scale Agents
```yaml
# workflows/scale-agents.yaml
apiVersion: provisioning/v1
kind: Workflow
spec:
description: "Dynamically scale agent pools based on queue depth"
steps:
- name: check-queue-depth
task: provisioning.query
params:
query: "SELECT queue_depth FROM agent_health WHERE role = '${AGENT_ROLE}'"
outputs: [queue_depth]
- name: decide-scaling
task: provisioning.evaluate
params:
condition: |
if queue_depth > 10 && current_replicas < max_replicas:
scale_to = min(current_replicas + 2, max_replicas)
action = "scale_up"
elif queue_depth < 2 && current_replicas > min_replicas:
scale_to = max(current_replicas - 1, min_replicas)
action = "scale_down"
else:
action = "no_change"
outputs: [action, scale_to]
- name: execute-scaling
task: provisioning.scale-taskserv
when: action != "no_change"
params:
taskserv: "vapora-agents-${AGENT_ROLE}"
replicas: "${scale_to}"
timeout: 5m
```
---
## 🎯 CLI Usage
```bash
cd provisioning/vapora-wrksp
# 1. Create cluster
provisioning cluster create --config kcl/cluster.k
# 2. Deploy full stack
provisioning workflow run workflows/deploy-full-stack.yaml
# 3. Check status
provisioning health-check --services all
# 4. Scale agents
provisioning taskserv scale vapora-agents-developer --replicas 10
# 5. Monitor
provisioning dashboard open # Grafana dashboard
provisioning logs tail -f vapora-backend
# 6. Upgrade
provisioning taskserv upgrade vapora-backend --image vapora/backend:0.3.0
# 7. Rollback
provisioning taskserv rollback vapora-backend --to-version 0.1.0
```
---
## 🎯 Implementation Checklist
- [ ] KCL schemas (cluster, services, storage, agents)
- [ ] Taskserv definitions (5 services)
- [ ] Workflows (deploy, scale, upgrade, disaster-recovery)
- [ ] Namespace creation + RBAC
- [ ] PVC provisioning (Rook Ceph)
- [ ] Service discovery (DNS, load balancing)
- [ ] Health checks + readiness probes
- [ ] Logging aggregation (ELK or similar)
- [ ] Secrets management (RustyVault integration)
- [ ] Monitoring (Prometheus metrics export)
- [ ] Documentation + runbooks
---
## 📊 Success Metrics
✅ Full VAPORA deployed < 1 hour
✅ All services healthy post-deployment
✅ Agent pools scale automatically
✅ Rollback works if deployment fails
✅ Monitoring captures all metrics
✅ Scaling decisions in < 1 min
---
**Version**: 0.1.0
**Status**: ✅ Integration Specification Complete
**Purpose**: Provisioning deployment of VAPORA infrastructure