Assembling the right context before the model runs — context compilers, budgets, KV-cache, and MCP federation.
11 articles
How prompt cache keys are derived across Anthropic, OpenAI, Gemini, vLLM, and SGLang, and the six agent mistakes that silently break the stable prefix.
Memory MCP servers compared: thirteen named options across knowledge-graph, vector, Markdown, and SQL storage, with pricing, and filtered by the local-first vs hosted decision.
The MCP 2026-07-28 release candidate deletes protocol sessions, the Mcp-Session-Id header, and the handshake. Agent memory moves to an external store.
Context budgeting allocates finite agent tokens across system, tools, retrieval, history, outputs, and response buffer.
Context optimization for AI agents unifies KV-cache hit rate, prefix stability, token budget, latency, cost, and placement into one runtime decision.
Where flow control ends and window assembly begins: the boundary between orchestrator and context compiler in LLM agent systems.
Deterministic context assembly improves cacheability and auditability; LLM-directed assembly adds adaptivity. Most agent systems need both.
Context engineering is the discipline. The context compiler is the per-turn runtime layer that ranks, budgets, secures, and assembles each model call.
Memory MCP servers explained: what they are, how to choose one by where data lives and what it does, and how to install so an agent remembers across sessions.
MCP federation in 2026: what gateways and the June 2025 spec solved for running multiple MCP servers, and which problems, like auth propagation, remain open.
How prompt-cache keys are canonicalized, why a stable prefix decides your KV-cache hit rate, and what each provider actually discounts on a cache read.