Production-Ready AI Agents: Fine-Tuning and Optimization

Building an AI agent prototype is only the first step; ensuring it performs reliably in real-world scenarios requires systematic optimization. This course guides you through the essential methodologies to transition your AI agents from fragile demos to robust, production-ready systems. You will master the foundational concepts of agent fine-tuning and learn how to identify quality bottlenecks, structure high-quality training datasets, and apply modern optimization strategies to improve agent behavior. What you'll learn: - Understand key terminology and the core architectural differences between basic prompting and model fine-tuning. - Compare fine-tuning methodologies including Supervised Fine-Tuning (SFT), Rejection Fine-Tuning (RFT), and Direct Preference Optimization (DPO). - Prepare and format high-quality training datasets tailored for specific agent tasks. - Identify common agent quality problems, alignment issues, and performance bottlenecks. - Configure hyperparameters and implement iterative evaluation strategies to measure agent reliability. - Apply modern evaluation frameworks and basic MLOps principles to maintain agent performance over time. This written course begins with foundational concepts of model tuning and agent architecture, then guides you step-by-step through dataset preparation, optimization algorithms, and systematic evaluation techniques. It is designed for software developers, data practitioners, and AI enthusiasts who want to understand the mechanics of agent optimization; no prior fine-tuning experience is required. Start reading today to build smarter, more reliable AI agents for real-world applications.