d14150da75
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.
88 lines
1.9 KiB
TOML
88 lines
1.9 KiB
TOML
[taskserv]
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name = "vapora-agents"
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type = "agent-orchestrator"
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version = "0.2.0"
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description = "VAPORA Agent Runtime (12 specialized roles)"
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[source]
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repository = "ssh://git@repo.jesusperez.pro:32225/jesus/Vapora.git"
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branch = "main"
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path = "vapora-agents/"
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[build]
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runtime = "rust"
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build_command = "cargo build --release -p vapora-agents"
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[deployment]
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namespace = "vapora-agents"
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replicas = 3
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image = "vapora/agents"
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image_tag = "${version}"
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[ports]
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service = 8089
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metrics = 9090
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[resources]
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requests = { cpu = "4000m", memory = "8Gi" }
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limits = { cpu = "8000m", memory = "16Gi" }
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[agent_pool]
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# Agents deployed by this taskserv
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agents = [
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"architect", "developer", "code-reviewer", "tester",
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"documenter", "marketer", "presenter",
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"devops", "monitor", "security",
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"project-manager", "decision-maker", "orchestrator"
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]
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max_concurrent_agents = 50
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queue_depth_warning = 100
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[dependencies]
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required = ["nats", "surrealdb"]
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optional = ["mcp-gateway", "llm-router"]
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[environment]
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NATS_URL = "nats://nats-0.vapora-system:4222"
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DATABASE_URL = "surrealdb://surrealdb-0.vapora-system:8000"
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AGENT_REGISTER_INTERVAL_SECS = "30"
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HEALTH_CHECK_INTERVAL_SECS = "15"
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RUST_LOG = "debug,vapora_agents=trace"
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[secrets]
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CLAUDE_API_KEY = "secret:vapora-secrets:claude-api-key"
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OPENAI_API_KEY = "secret:vapora-secrets:openai-api-key"
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GEMINI_API_KEY = "secret:vapora-secrets:gemini-api-key"
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ANTHROPIC_KEY = "secret:vapora-secrets:anthropic-key"
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[scaling]
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min_replicas = 3
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max_replicas = 20
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target_cpu = 75
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target_memory = 80
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scale_down_delay_secs = 300
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[health_check]
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type = "http"
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path = "/health"
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interval_secs = 10
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timeout_secs = 5
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failure_threshold = 3
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[persistence]
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enabled = true
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size = "20Gi"
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storage_class = "ssd"
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mount_path = "/agent-state"
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[update_strategy]
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type = "RollingUpdate"
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max_surge = 2
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max_unavailable = 0
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min_ready_seconds = 60
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[monitoring]
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prometheus_metrics = true
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trace_sampling = 0.1
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