Advanced Patterns
Overviewโ
Advanced design patterns for continuously improving production Agentic AI system performance. The Self-Improving Agent Loop provides a closed-loop architecture that combines human feedback with automated evaluation to enhance agent behavior, and ADR documents record the rationale and trade-offs for design decisions. Knowledge Feature Store covers 3-plane feature management combining ontologies and Knowledge Graphs. The Semantic Caching strategy has moved to the Inference Optimization category.
Document Listโ
๐๏ธ Self-Improving Loop
5-stage loop design and safety mechanisms for self-hosted SLMs to autonomously learn and improve from production traces based on Karpathy's autosearch concept
๐๏ธ ADR: Self-Improving Loop
Architecture Decision Record documenting principles, scope, responsibilities, and rollback boundaries to be agreed upon before introducing the Self-Improving Agent Loop to production
๐๏ธ Knowledge Feature Store
3-plane design integrating ontology and Knowledge Graph into traditional Feature Store to reduce hallucinations, enable provenance tracking, and enhance domain entity utilization
๐๏ธ MCP Tool Token Optimization Patterns
Token optimization patterns for MCP-based agents. Quantifies upfront loading overhead and reduces token costs by 70-98% through four techniques โ Progressive Discovery, tool compression proxy, Code Execution, and prompt cache alignment.