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Profa
b6fe4abec8
- Fix MCP entry path resolution in mcp.ts (1 level up, not 2) - Remove stale v3/ directory references in 10-workers.ts - Fix CRLF line endings in 170+ skill files breaking frontmatter parsing - Add CRLF→LF safety net in prepare-assets.sh - Rewrite README.md for clarity (1097→280 lines, outcome-focused) - Sync assets from .claude/skills via prepare-assets.sh Fixes issues reported in v3.7.9 upgrade (skill-lint failures, MCP errors) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
63 lines
2.9 KiB
JSON
63 lines
2.9 KiB
JSON
{
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"pattern": "ADR-051 Complete Integration Architecture",
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"namespace": "adr-051/summary",
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"created": "2026-01-20",
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"version": "1.0",
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"description": "End-to-end integration of all four components: Agent Booster, Model Router, ONNX Embeddings, ReasoningBank",
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"components": [
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"Agent Booster (WASM transforms)",
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"Model Router (5-tier complexity)",
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"ONNX Embeddings (local generation)",
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"ReasoningBank (trajectory learning)"
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],
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"integration_flow": {
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"phase_1_receive": "Task arrives at coordinator",
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"phase_2_analyze": "Calculate complexity score and collect embeddings",
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"phase_3_route": "Model router checks booster eligibility first, then selects model tier",
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"phase_4_execute": "Execute via Agent Booster (WASM) or model tier (API)",
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"phase_5_record": "Track trajectory in ReasoningBank with quality score",
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"phase_6_learn": "Extract patterns, apply quality gates, share with agents"
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},
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"performance_outcomes": {
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"simple_transforms": "<1ms via booster ($0 cost)",
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"avg_task_latency": "2-5s with intelligent routing",
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"cost_savings": "40-60% reduction vs naive routing",
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"quality_improvement": "5-15% better outcomes with learned patterns"
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},
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"reliability_characteristics": {
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"fallback_layers": "Booster → Model tier → Human review",
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"error_recovery": "Retry with degraded model if needed",
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"quality_threshold": "Reject low-confidence results",
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"audit_trail": "Full trajectory logging for compliance"
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},
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"scaling_properties": {
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"multi_agent": "Distributed across swarm",
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"knowledge_sharing": "Patterns propagate via gossip protocol",
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"adaptive_routing": "Learns optimal paths over time",
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"load_distribution": "Auto-scale based on demand"
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},
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"deployment_checklist": [
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"Agent Booster WASM module deployed",
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"Model router API integrated",
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"ONNX embedding service running",
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"ReasoningBank trajectory database initialized",
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"Quality gate thresholds configured",
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"Pattern storage and retrieval working",
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"Cross-agent sharing channels open",
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"Monitoring and metrics collection active"
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],
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"key_patterns": {
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"booster_patterns": "14 files in .agentic-qe/patterns/adr-051-booster-patterns.json",
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"router_patterns": "3 files in .agentic-qe/patterns/adr-051-router-patterns.json",
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"embedding_patterns": "4 files in .agentic-qe/patterns/adr-051-embedding-patterns.json",
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"reasoning_patterns": "4 files in .agentic-qe/patterns/adr-051-reasoning-patterns.json"
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},
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"successRate": 0.92,
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"lastUpdated": "2026-01-20",
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"implementation_status": "Production Ready",
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"cross_session_learning_enabled": true,
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"pattern_retrieval_strategy": "Load from JSON files in .agentic-qe/patterns/ on session start",
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"pattern_update_strategy": "Append new patterns, maintain version history",
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"memory_coordination_notes": "All patterns stored persistently for multi-session learning and agent coordination"
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}
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