Optimizing GenAI and Agentic AI: Balancing Cost, Quality, and Latency in Production Building production-ready GenAI and Agentic systems is no longer about who can write the best prompt. It has become a complex engineering challenge of balancing the “Iron Triangle”: Cost, Quality, and Latency. As we move from single-prompt chatbots to autonomous agents that can call tools and reason through multi-step tasks, the overhead grows exponentially. If you do not optimize for these three pillars early, your application will either be too slow for users, too expensive to maintain, or too unreliable for the enterprise. Deploying a GenAI system in production is not a software problem anymore. It is an economics problem dressed up in a transformer architecture. Every token you process costs money, every guardrail adds latency, and every shortcut risk quality. This piece is a practical map of that territory — where the landmines are, and how engineers who have already stepped on a few of them learne...
