/** * QE Task Router * ADR-022: Adaptive QE Agent Routing * * ML-based task routing that combines: * - Vector similarity (semantic matching via transformer embeddings) * - Historical performance (agent success rates from feedback) * - Capability matching (task requirements vs agent capabilities) */ import type { QETask, QERoutingDecision, QERouterConfig } from './types.js'; /** * QE Task Router that uses ML-based routing to select optimal agents */ export declare class QETaskRouter { private config; private agentEmbeddings; private initialized; private embeddingCache; constructor(config?: Partial); /** * Initialize router by computing agent embeddings */ initialize(): Promise; /** * Build text representation of agent for embedding */ private buildAgentEmbeddingText; /** * Route a task to the optimal agent */ route(task: QETask): Promise; /** * Detect QE domain from task description */ private detectDomain; /** * Detect required capabilities from task description */ private detectCapabilities; /** * Filter candidate agents based on hard requirements */ private filterCandidates; /** * Check if complexity matches agent's range */ private complexityMatches; /** * Get task embedding (with caching) */ private getTaskEmbedding; /** * Score an agent for a task */ private scoreAgent; /** * Build human-readable reasoning for routing decision */ private buildReasoning; /** * Update agent performance based on feedback */ updateAgentPerformance(agentId: string, success: boolean, qualityScore: number, durationMs: number): void; /** * Get router statistics */ getStats(): { initialized: boolean; agentCount: number; embeddingsCached: number; config: QERouterConfig; }; /** * Clear embedding cache */ clearCache(): void; } /** * Create a new QE task router instance */ export declare function createQETaskRouter(config?: Partial): QETaskRouter; //# sourceMappingURL=qe-task-router.d.ts.map