Agentic AI: The New Software Paradigm - Part 3 of 3

This course is Part 3, the concluding chapter of the "Agentic AI: The New Software Paradigm" three-part-series. After building a foundation in Part 1 and exploring patterns and frameworks in Part 2, Part 3 is about taking an agent from a working prototype to something you can actually run, trust and improve over time.

You'll start with local models - running and working with models outside of hosted APIs, useful for cost, privacy, or control. From there, the course tackles one of the hardest problems in agentic AI: evals. You'll learn how to design evaluations that genuinely measure whether an agent is doing its job, not just producing plausible-looking output.

Next comes observability: the tracing, logging and debugging practices that let you understand what an agent actually did and why, especially once it's running in production. The course then covers fine-tuning and distillation, showing how to adapt or shrink models for your specific use case rather than relying solely on general-purpose defaults.

The course closes with a concluding outlook, tying the whole three-part series together and looking at where agentic AI is heading next.

This course is Part 3 of 3 in the "Agentic AI: The New Software Paradigm" series:

- Part 1, "Agentic AI: The New Software Paradigm - Part 1": Foundations & Mental Models, Architecture & System Design, Core Agentic Building Blocks

- Part 2, "Agentic AI: The New Software Paradigm - Part 2": Patterns, Frameworks & Ecosystem

- Part 3 (this course): Deploy, Evaluate, Improve

Search for the exact titles above on Udemy to find the other parts and get the full picture of agentic AI, from first principles to production.