context-engineering

Designing a scalable context pipeline involves structuring the full 8-layer context stack from L1 to L8 to maximize signal, safety, and efficiency. This approach defines memory strategies, grounding requirements, and strict output contracts for reliable AI deployments.

The 8-layer context stack is an architectural framework encompassing policy, memory, RAG, and grounding layers. It organizes the AI context pipeline to enforce token budgets, cacheable prefixes, and strict output contracts for optimized system reliability.

You enforce token budgets and grounding envelopes by applying context engineering constraints within your pipeline design. This ensures the AI system operates within defined safety limits, maximizing signal efficiency while maintaining strict output contracts.

Yes, context engineering supports multi-step orchestration and memory design as core features. It allows you to plan retrieval strategies, structure memory layers, and orchestrate complex workflows within a unified context stack.

The best way to plan retrieval and RAG is to integrate them into the broader context stack design as defined layers. This ensures retrieval pipelines align with token budgets, cacheable prefixes, and grounding policies to maximize signal and safety.