How a 'Before Model' Callback Let 10,000 AI Agents Run Without Burning Tokens

A software architect building a 10,000-agent simulation found that calling an LLM for every agent decision was too slow, costly, and unpredictable to scale. The solution was to use the Agent Development Kit's 'before model' callback to intercept LLM calls and replace them with fast, deterministic Python code instead. An LLM was still used during the development phase — specifically Google's Gemini — to research algorithms and generate a multi-phase route-solving approach for a 26.2-mile marathon path, but it was never invoked at runtime. This architectural pattern, borrowed from game development's 'server tick' model, keeps full observability and agent lifecycle management while eliminating token costs and latency. The key takeaway is that AI should act as the architect during design, not the executor during runtime, so that scaling agent count does not mean scaling token spend.