Designing Hybrid Context Layers

Architects the right retrieval strategy for every query — teaching your agent when to use RAG, a knowledge graph, or a temporal index instead of defaulting to vector search for everything.

Install
cmdop skills install agensi-designing-hybrid-context-layers

Designing Hybrid Context Layers is a skill from Agensi that addresses a structural flaw common in RAG-based agent architectures: treating every query as a simple document lookup. Most retrieval failures are not model failures — they result from mismatched retrieval strategy and query type.

The skill defines a three-layer context model. Layer 1 is a Factual Store using Vector RAG, appropriate only for single-fact or single-document point queries. Layer 2 is a Relational Store backed by a knowledge graph, designed for entity relationships, dependency chains, and multi-hop queries that require traversal across linked nodes — for example, identifying which teams own services that depend on a deprecated API. Layer 3 is a Temporal/Episodic Store using a timeline index, which handles event sequences, causal decision chains, and questions like “What decisions led to this incident?” that require timestamped structure.

The skill also delivers a query router decision tree — a concrete classification step that determines which layer handles each incoming query before any retrieval begins. This prevents silent failures such as routing relational queries through vector search, which produces degraded answers as context volume grows.

A phased implementation roadmap is included for teams migrating from RAG-only systems. The skill targets teams building AI agents over enterprise knowledge bases, architecture decision records, or incident histories where relational or causal answers are required, not just fact lookups.

Use cases

  • Route multi-hop dependency queries to a knowledge graph instead of vector search
  • Design a temporal index to answer causal questions about incident histories
  • Build a query classification step that selects the correct retrieval layer before retrieval begins
  • Migrate an existing RAG-only agent architecture to a three-layer hybrid retrieval system
  • Reduce hallucinations caused by long context with no causal structure
  • Answer causal decision-chain questions over architecture decision records using a timeline index

When to use it

  • An agent needs to answer relational or multi-hop questions across linked entities
  • The system must trace causal event chains or decision histories, not just retrieve documents
  • A RAG-only system is producing degraded or silent failures on non-lookup queries
  • The team is building over enterprise knowledge bases, incident logs, or architecture records
  • A structured query routing step is needed before retrieval begins

When not to use it

  • The agent only needs to answer simple single-document fact lookups, where vector RAG alone is sufficient
  • The team has no infrastructure to support a knowledge graph or timeline index alongside vector search
  • The use case does not involve relational, causal, or multi-hop queries at all
  • Looking for a ready-to-run MCP server or executable tool — this skill provides architectural guidance, not runtime tooling