Why Just-in-Time Context Is the Secret to Efficient AI Agents

The core claim is simple: AI agent development should abandon pre‑loading and pre‑configuring everything and instead adopt a "just‑in‑time context" approach, where relevant information is injected only at the moment it is required.

The author illustrates the idea with everyday analogies—buying only the items you need at a convenience store or adding ingredients to a hot pot step by step—showing that eager loading wastes resources while on‑demand loading is efficient.

For prompts, the recommended practice is to feed the model a prompt only when the specific task arises (e.g., providing code‑related prompts during coding and debugging prompts during bug fixing), which the author describes as "precise targeting" rather than "spraying the model with all possible prompts".

Skills are portrayed as higher‑level packages of prompts. When a skill is invoked, a predefined prompt bundle is triggered at that exact moment, avoiding the need to load all skills upfront and preventing context‑window overflow.

The article highlights the Model Context Protocol (MCP) introduced by Claude Code, which now supports dynamic discovery: the model discovers and connects to required MCP services during execution instead of relying on static configuration files.

Configuration files such as CLAUDE.md and AGENTS.md are shown to follow the same just‑in‑time principle. Their contents are loaded only when the model navigates into the corresponding sub‑directory, akin to opening nested Russian dolls, which prevents the context window from being exhausted in large projects.

This design shift turns AI agents from "garbage code generators" into "production‑grade tools" by reducing token usage, cutting compute costs, and improving response relevance.

Potential extensions include on‑demand loading of long‑term memory, dynamic composition of multiple tools into a single capability, real‑time knowledge‑base retrieval, and even just‑in‑time model switching based on task complexity.

For developers, the author advises a modular architecture with dynamic loading mechanisms, clear layering, and well‑defined boundaries, which yields lighter, faster, and more flexible agents—comparable to a cheetah rather than a sluggish elephant—and also lowers operational costs.

Finally, the author predicts that just‑in‑time context will become a standard paradigm for AI agent development, much like RESTful APIs became the norm for web services, and that frameworks and platforms will continue to evolve around this principle.