As agentic systems move from prototype to production, managing inference costs, latency, and throughput becomes a real engineering constraint and not just a trade-off. This session breaks down three practical architectural patterns used to optimize large language model operations in autonomous agents, with a focus on how they hold up under real production conditions. Key takeaways: → Query-aware routing to dynamically match tasks to the most efficient model without sacrificing capability → Semantic caching strategies to bypass redundant model calls and reduce latency at scale → Prompt design techniques for more token-efficient reasoning while maintaining output quality Along the way, we’ll expose where these approaches break down in production and what changes when systems are operating under real load.