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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
use tracing::{info_span, warn_span, Span};
use std::time::Instant;
/// Span context for task execution tracing
pub struct TaskSpan {
span: Span,
start: Instant,
}
impl TaskSpan {
/// Create a new task execution span
pub fn new(task_id: &str, agent_id: &str, task_type: &str) -> Self {
let span = info_span!(
"task_execution",
task_id = %task_id,
agent_id = %agent_id,
task_type = %task_type,
duration_ms = tracing::field::Empty,
);
Self {
span,
start: Instant::now(),
}
}
/// Get reference to span for instrumentation
pub fn span(&self) -> &Span {
&self.span
}
/// Record span completion with duration
pub fn complete(self) {
let duration_ms = self.start.elapsed().as_millis() as u64;
self.span.record("duration_ms", duration_ms);
}
/// Record span completion with error
pub fn error(self, error_msg: &str) {
let duration_ms = self.start.elapsed().as_millis() as u64;
self.span.record("duration_ms", duration_ms);
tracing::error!(
parent: &self.span,
error = %error_msg,
"Task execution failed"
);
}
}
/// Span context for agent operations
pub struct AgentSpan {
span: Span,
}
impl AgentSpan {
/// Create span for agent registration
pub fn registration(agent_id: &str, role: &str) -> Self {
let span = info_span!(
"agent_registration",
agent_id = %agent_id,
role = %role,
);
Self { span }
}
/// Create span for agent status update
pub fn status_update(agent_id: &str, load: f64, available: bool) -> Self {
let span = info_span!(
"agent_status_update",
agent_id = %agent_id,
load = load,
available = available,
);
Self { span }
}
/// Create span for agent heartbeat
pub fn heartbeat(agent_id: &str) -> Self {
let span = info_span!(
"agent_heartbeat",
agent_id = %agent_id,
);
Self { span }
}
/// Get reference to span
pub fn span(&self) -> &Span {
&self.span
}
}
/// Span context for routing operations
pub struct RoutingSpan {
span: Span,
start: Instant,
}
impl RoutingSpan {
/// Create span for provider selection
pub fn provider_selection(task_type: &str, candidates: usize) -> Self {
let span = info_span!(
"provider_selection",
task_type = %task_type,
candidate_count = candidates,
selected_provider = tracing::field::Empty,
);
Self {
span,
start: Instant::now(),
}
}
/// Create span for cost calculation
pub fn cost_calculation(provider: &str) -> Self {
let span = info_span!(
"cost_calculation",
provider = %provider,
input_tokens = tracing::field::Empty,
output_tokens = tracing::field::Empty,
total_cost = tracing::field::Empty,
);
Self {
span,
start: Instant::now(),
}
}
/// Record selected provider
pub fn record_selection(&self, provider: &str) {
self.span.record("selected_provider", provider);
}
/// Record cost details
pub fn record_cost(&self, input_tokens: u64, output_tokens: u64, cost: f64) {
self.span.record("input_tokens", input_tokens);
self.span.record("output_tokens", output_tokens);
self.span.record("total_cost", cost);
}
/// Complete routing operation
pub fn complete(self) {
let duration_ms = self.start.elapsed().as_millis() as u64;
tracing::debug!(
parent: &self.span,
duration_ms = duration_ms,
"Routing decision completed"
);
}
/// Get reference to span
pub fn span(&self) -> &Span {
&self.span
}
}
/// Span context for swarm operations
pub struct SwarmSpan {
span: Span,
start: Instant,
}
impl SwarmSpan {
/// Create span for task assignment
pub fn task_assignment(task_id: &str, assigned_to: &str) -> Self {
let span = info_span!(
"swarm_task_assignment",
task_id = %task_id,
assigned_to = %assigned_to,
duration_ms = tracing::field::Empty,
);
Self {
span,
start: Instant::now(),
}
}
/// Create span for coalition formation
pub fn coalition_formation(coalition_id: &str, required_roles: usize) -> Self {
let span = info_span!(
"swarm_coalition_formation",
coalition_id = %coalition_id,
required_roles = required_roles,
members_recruited = tracing::field::Empty,
);
Self {
span,
start: Instant::now(),
}
}
/// Create span for consensus voting
pub fn consensus_voting(proposal_id: &str, voter_count: usize) -> Self {
let span = info_span!(
"swarm_consensus",
proposal_id = %proposal_id,
voter_count = voter_count,
consensus_reached = tracing::field::Empty,
);
Self {
span,
start: Instant::now(),
}
}
/// Record members recruited for coalition
pub fn record_members(&self, count: usize) {
self.span.record("members_recruited", count);
}
/// Record consensus result
pub fn record_consensus(&self, reached: bool) {
self.span.record("consensus_reached", reached);
}
/// Complete swarm operation
pub fn complete(self) {
let duration_ms = self.start.elapsed().as_millis() as u64;
self.span.record("duration_ms", duration_ms);
}
/// Get reference to span
pub fn span(&self) -> &Span {
&self.span
}
}
/// Span context for analytics operations
pub struct AnalyticsSpan {
span: Span,
}
impl AnalyticsSpan {
/// Create span for event processing
pub fn event_processing(event_type: &str) -> Self {
let span = info_span!(
"analytics_event_processing",
event_type = %event_type,
processed = false,
);
Self { span }
}
/// Create span for alert generation
pub fn alert_generation(alert_type: &str, severity: &str) -> Self {
let span = warn_span!(
"analytics_alert",
alert_type = %alert_type,
severity = %severity,
);
Self { span }
}
/// Create span for aggregation
pub fn aggregation(window_name: &str) -> Self {
let span = info_span!(
"analytics_aggregation",
window = %window_name,
aggregated_count = tracing::field::Empty,
);
Self { span }
}
/// Record aggregation count
pub fn record_count(&self, count: usize) {
self.span.record("aggregated_count", count);
}
/// Get reference to span
pub fn span(&self) -> &Span {
&self.span
}
}
/// Span context for knowledge graph operations
pub struct KGSpan {
span: Span,
start: Instant,
}
impl KGSpan {
/// Create span for execution recording
pub fn record_execution(task_id: &str, agent_id: &str) -> Self {
let span = info_span!(
"kg_record_execution",
task_id = %task_id,
agent_id = %agent_id,
duration_ms = tracing::field::Empty,
);
Self {
span,
start: Instant::now(),
}
}
/// Create span for similarity query
pub fn similarity_query(query_text: &str) -> Self {
let span = info_span!(
"kg_similarity_query",
query_length = query_text.len(),
matches_found = tracing::field::Empty,
);
Self {
span,
start: Instant::now(),
}
}
/// Create span for reasoning operation
pub fn reasoning(operation: &str) -> Self {
let span = info_span!(
"kg_reasoning",
operation = %operation,
insights_generated = tracing::field::Empty,
);
Self {
span,
start: Instant::now(),
}
}
/// Record number of insights
pub fn record_insights(&self, count: usize) {
self.span.record("insights_generated", count);
}
/// Record number of matches
pub fn record_matches(&self, count: usize) {
self.span.record("matches_found", count);
}
/// Complete operation
pub fn complete(self) {
let duration_ms = self.start.elapsed().as_millis() as u64;
self.span.record("duration_ms", duration_ms);
}
/// Get reference to span
pub fn span(&self) -> &Span {
&self.span
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_task_span_creation() {
let span = TaskSpan::new("task-1", "agent-1", "coding");
// Span created successfully
let _ = span.span();
}
#[test]
fn test_agent_span_registration() {
let span = AgentSpan::registration("agent-1", "developer");
// Span created successfully
let _ = span.span();
}
#[test]
fn test_routing_span_selection() {
let span = RoutingSpan::provider_selection("code_generation", 3);
span.record_selection("claude");
// Span should have recorded the provider selection
}
#[test]
fn test_swarm_span_coalition() {
let span = SwarmSpan::coalition_formation("coal_123", 3);
span.record_members(3);
// Span should have recorded member count
}
#[test]
fn test_kg_span_reasoning() {
let span = KGSpan::reasoning("pattern_detection");
span.record_insights(5);
// Span should have recorded insights
}
}