Agentic AI: The New Software Paradigm – Part 3 of 3, Deploy, evaluate & improve agents: local models, evals, observability, fine-tuning (Part 3 of 3).
Course Description
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.
Who this course is for:
- Developers and AI engineers ready to deploy, evaluate and continuously improve real agentic systems
