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.
264 lines
7.0 KiB
Plaintext
264 lines
7.0 KiB
Plaintext
"""
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VAPORA Agent Pools Configuration
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Defines scaling policies and configurations for each of the 12 agent roles
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"""
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import k.api.all as k
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# ===== AGENT POOL DEFINITIONS =====
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agent_pools = {
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"architect": {
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role = "Architect"
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description = "System design and architecture decisions"
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llm = "Claude Opus"
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parallelizable = false # Initiator role, must run sequentially
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min_replicas = 2
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max_replicas = 5
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target_cpu = 70
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cpu_request = "2000m"
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memory_request = "4Gi"
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cpu_limit = "4000m"
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memory_limit = "8Gi"
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}
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"developer": {
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role = "Developer"
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description = "Code implementation"
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llm = "Claude Sonnet"
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parallelizable = true
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min_replicas = 5
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max_replicas = 20
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target_cpu = 60
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cpu_request = "2000m"
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memory_request = "3Gi"
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cpu_limit = "4000m"
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memory_limit = "6Gi"
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}
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"code_reviewer": {
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role = "CodeReviewer"
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description = "Code quality and review"
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llm = "Claude Sonnet"
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parallelizable = true
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min_replicas = 3
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max_replicas = 10
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target_cpu = 65
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cpu_request = "1500m"
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memory_request = "2Gi"
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cpu_limit = "3000m"
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memory_limit = "4Gi"
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}
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"tester": {
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role = "Tester"
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description = "Test writing and validation"
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llm = "Claude Sonnet"
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parallelizable = true
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min_replicas = 3
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max_replicas = 10
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target_cpu = 70
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cpu_request = "2000m"
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memory_request = "3Gi"
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cpu_limit = "4000m"
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memory_limit = "6Gi"
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}
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"documenter": {
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role = "Documenter"
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description = "Documentation and guides"
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llm = "GPT-4"
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parallelizable = true
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min_replicas = 2
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max_replicas = 8
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target_cpu = 50
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cpu_request = "1000m"
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memory_request = "2Gi"
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cpu_limit = "2000m"
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memory_limit = "4Gi"
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}
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"marketer": {
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role = "Marketer"
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description = "Marketing content and campaigns"
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llm = "Claude Sonnet"
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parallelizable = true
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min_replicas = 1
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max_replicas = 5
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target_cpu = 40
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cpu_request = "1000m"
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memory_request = "2Gi"
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cpu_limit = "2000m"
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memory_limit = "4Gi"
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}
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"presenter": {
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role = "Presenter"
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description = "Presentations and slides"
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llm = "Claude Sonnet"
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parallelizable = true
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min_replicas = 1
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max_replicas = 3
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target_cpu = 50
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cpu_request = "1000m"
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memory_request = "2Gi"
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cpu_limit = "2000m"
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memory_limit = "4Gi"
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}
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"devops": {
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role = "DevOps"
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description = "CI/CD and deployment"
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llm = "Claude Sonnet"
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parallelizable = true
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min_replicas = 2
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max_replicas = 8
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target_cpu = 60
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cpu_request = "1500m"
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memory_request = "2Gi"
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cpu_limit = "3000m"
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memory_limit = "4Gi"
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}
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"monitor": {
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role = "Monitor"
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description = "Health checking and alerting"
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llm = "Gemini Flash"
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parallelizable = true # Real-time monitoring
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min_replicas = 2
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max_replicas = 5
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target_cpu = 30
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cpu_request = "1000m"
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memory_request = "1Gi"
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cpu_limit = "2000m"
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memory_limit = "2Gi"
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}
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"security": {
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role = "Security"
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description = "Security audit and verification"
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llm = "Claude Opus"
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parallelizable = false # Can block pipeline
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min_replicas = 2
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max_replicas = 5
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target_cpu = 70
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cpu_request = "2000m"
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memory_request = "4Gi"
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cpu_limit = "4000m"
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memory_limit = "8Gi"
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}
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"project_manager": {
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role = "ProjectManager"
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description = "Project tracking and roadmap"
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llm = "Claude Sonnet"
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parallelizable = true
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min_replicas = 1
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max_replicas = 3
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target_cpu = 40
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cpu_request = "1000m"
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memory_request = "2Gi"
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cpu_limit = "2000m"
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memory_limit = "4Gi"
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}
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"decision_maker": {
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role = "DecisionMaker"
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description = "Conflict resolution and decisions"
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llm = "Claude Opus"
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parallelizable = false # On-demand decision making
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min_replicas = 1
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max_replicas = 3
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target_cpu = 70
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cpu_request = "2000m"
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memory_request = "4Gi"
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cpu_limit = "4000m"
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memory_limit = "8Gi"
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}
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}
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# ===== HORIZONTAL POD AUTOSCALERS =====
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hpas = [
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{
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name = "vapora-agents-developer-hpa"
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target_deployment = "vapora-agents"
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agent_role = "developer"
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min_replicas = 5
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max_replicas = 20
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target_cpu_utilization = 60
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metrics = [
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{
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type = "Resource"
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resource = {
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name = "cpu"
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target = {type = "Utilization", averageUtilization = 60}
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}
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},
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{
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type = "Pods"
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pods = {
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metric = {name = "agent_queue_depth"}
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target = {type = "AverageValue", averageValue = "50"}
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}
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}
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]
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}
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{
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name = "vapora-agents-reviewer-hpa"
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target_deployment = "vapora-agents"
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agent_role = "code_reviewer"
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min_replicas = 3
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max_replicas = 10
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target_cpu_utilization = 65
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}
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{
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name = "vapora-agents-monitor-hpa"
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target_deployment = "vapora-agents"
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agent_role = "monitor"
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min_replicas = 2
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max_replicas = 5
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target_cpu_utilization = 30
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}
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]
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# ===== POD DISRUPTION BUDGETS =====
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pod_disruption_budgets = [
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{
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name = "vapora-agents-pdb"
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selector = {matchLabels = {"app": "vapora-agents"}}
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minAvailable = 2 # Always keep at least 2 agents running
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}
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{
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name = "surrealdb-pdb"
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selector = {matchLabels = {"app": "surrealdb"}}
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minAvailable = 2 # Database must always have 2+ replicas
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}
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]
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# ===== NETWORK POLICIES FOR AGENTS =====
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network_policies = [
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{
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name = "allow-agent-to-nats"
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ingress = [{
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from = [{podSelector = {matchLabels = {"app": "vapora-agents"}}}]
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ports = [{protocol = "TCP", port = 4222}]
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}]
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egress = [{
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to = [{podSelector = {matchLabels = {"app": "nats"}}}]
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ports = [{protocol = "TCP", port = 4222}]
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}]
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}
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{
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name = "allow-agent-to-database"
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ingress = [{
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from = [{podSelector = {matchLabels = {"app": "vapora-agents"}}}]
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ports = [{protocol = "TCP", port = 8000}]
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}]
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egress = [{
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to = [{podSelector = {matchLabels = {"app": "surrealdb"}}}]
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ports = [{protocol = "TCP", port = 8000}]
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}]
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}
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]
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# ===== OUTPUT =====
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output = {
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agent_pools = agent_pools
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hpas = hpas
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pdbs = pod_disruption_budgets
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network_policies = network_policies
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}
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