Context Layer
A comprehensive technical series exploring the architecture, evaluation, 4 functional layers, and data engineering of Context Layers for AI Agents.
All Posts (7)
Ordered by PartContext Layer #1: Why Your Agent Fails
At their core, AI agents are just LLM loops wrapped in context and tools. Discover why the context layer is the true bottleneck of agent performance, and why context quality matters more than unconstrained planning.
Context Layer #2: Evals, Evals, Evals
Before writing custom data pipelines or context abstractions, you must answer one core question: How do you measure what is effective? Discover the 4 tiers of agent evaluation and benchmark-driven context engineering.
Context Layer #3: The 4 Layer Architecture Blueprint
Learn how to structure a Context Layer into four functional layers around a real-world CRM scenario: Raw Data, Analytical Data, Preprocessed Signals, and Semantic High-Level Memory.
Context Layer #4: Ingesting Raw Operational Data
Build Layer 1 with reliable ingestion, domain modeling, and dedicated indexes that unify operational data for AI Agents.
Context Layer #5: Governing Analytical Context
Build a governed analytical interface that gives AI Agents clear metric definitions, uncertainty, freshness, and evidence for business decisions.
Context Layer #6: Building Versioned Preprocessed Signals
Build reliable preprocessed signals with source evidence, explicit versions, safe failure states, document extraction, and evaluation gates.
Context Layer #7: Building Evidence Backed Memory
Build a revisable memory and retrieval plane that keeps evidence, claims, time, contradictions, and authoritative state distinct.