The reference guide to context infrastructure.
Understand the technologies, concepts, categories, and companies building the information layer for artificial intelligence.
Canonical Concepts
Core vocabulary and architectural patterns defining the ecosystem
A technical mechanism for retaining, retrieving, and managing state across an AI agent's interactions over both short and long time horizons, simulating cognitive architectures.
The overarching discipline of designing, structuring, retrieving, budgeting, and dynamically injecting the optimal state and information into a Large Language Model's prompt window.
A structured, explicit network representation of real-world entities, concepts, and relationships stored as nodes and edges in a graph database.
An open standard protocol (MCP) that unifies and securely standardizes how AI models and applications connect to external data repositories, databases, and tool servers.
A formal specification of conceptual classes, properties, constraints, and relationships within a specific domain, serving as a structural blueprint for knowledge representation.
An architectural pattern that dynamically retrieves relevant external information and injects it into an LLM's prompt window to ground text generation in factual, up-to-date knowledge.
An abstraction layer that maps complex, technical database schemas and raw data into consistent, business-friendly concepts accessible to both humans and AI agents.
Retrieval mechanism based on calculating mathematical distances between high-dimensional vector embeddings of queries and documents.
Market Categories
How commercial infrastructure products are segmented
Developer toolkits, runtime state machines, and execution engines for defining, coordinating, and executing autonomous single-agent and multi-agent workflows.
Software platforms, daemon processes, and APIs that manage stateful, persistent memory structures for AI agents across multiple sessions and tasks.
Hardware-accelerated and middleware caching solutions that store static prompt KV states and semantic query responses to reduce inference latency and token costs.
Testing frameworks, synthetic dataset generators, and benchmarking suites designed to quantitatively evaluate retrieval accuracy, context precision, long-context recall, and hallucination rates in AI pipelines.
Middleware and runtime systems that dynamically select, prune, compress, reorder, and inject relevant context into model context windows under token budget and latency constraints.
Observability platforms, tracing frameworks, and security gateways for monitoring prompt context flows, evaluating retrieval fidelity, detecting prompt injections, and auditing data privacy.
Directory Inventory
Vendors, platforms, and open-source projects
Observability and testing platform for monitoring AI agent context windows and cost.
Query language and chrome engine for extracting structured web context for AI agents.
Search and discovery platform offering NeuralSearch combining keyword and vector retrieval.
AI research lab creating Claude, Model Context Protocol (MCP), and Prompt Caching.
AI observability and evaluation platform offering Phoenix for open-source RAG tracing.
Cloud provider offering Bedrock Knowledge Bases for managed RAG and agent context.
Enterprise Chinese language web retrieval and semantic search engine platform.
Enterprise platform for evaluating, logging, and refining LLM prompts and context quality.
Editorial & Research
The State of Context Infrastructure (2026)
An empirical report on how context engineering, vector retrieval, graph RAG, and MCP are redefining the AI software stack.
Architecture GuideThe Agentic Data Stack: Context Infrastructure as the Evolution of Analytics Engineering
How the evolution from on-prem databases to cloud warehouses and modern data stacks culminates in context infrastructure, semantic layers, and agent harnesses.
Architecture GuideArchitecting Agent Memory: Working, Episodic, and Long-Term Systems
A comprehensive architectural guide to designing multi-tier memory systems, virtual memory paging, and temporal entity graphs for autonomous AI agents.
Architecture GuideThe Architecture of Context Engineering
A technical breakdown of context compilation, token budgeting, multi-stage retrieval, and KV-cache optimization in production AI systems.