Context Builder & Orchestration
Assembling the right context before the model runs — context compilers, budgets, KV-cache, and MCP federation.
8 articles
Context Budgeting: Zones, Allocation & Eviction
Context budgeting allocates finite agent tokens across system, tools, retrieval, history, outputs, and response buffer.
Context Optimizer: Cache, Budget & Placement
Context optimization for AI agents unifies KV-cache hit rate, prefix stability, token budget, latency, cost, and placement into one runtime decision.
Context Compiler vs Orchestration
Where flow control ends and window assembly begins: the boundary between orchestrator and context compiler in LLM agent systems.
Deterministic vs LLM Context Assembly
Deterministic context assembly improves cacheability and auditability; LLM-directed assembly adds adaptivity. Most agent systems need both.
Context Engineering Needs a Compiler
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: How to Give AI Agents Persistent Memory
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
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.
KV-Cache Hit Rate: The #1 Agent Metric
KV-cache hit rate is the top AI-agent metric: every major provider discounts a cache read 50–90% off fresh input, so context engineering is memory engineering.