The Agentic AI Engineering Course That Produces Working Production Systems

Standard AI development uses LLMs as tools within human-controlled workflows: a developer calls the API, processes the result, and decides what to do next. Agentic AI engineering builds systems where the AI itself drives the workflow: it determines what information it needs, what actions to take, how to coordinate with other AI components, and how to handle unexpected situations. Agentic engineering requires architectural thinking about autonomy, reliability, and safety that standard AI API usage does not.

This course produces a complete agentic AI engineering system in Python: the Glass-Box Context Engine with semantic blueprint generation, MCP orchestration with specialised agent servers, high-fidelity RAG with citation tracking, episodic memory management, prompt injection safeguards, output validation, and production deployment configuration. The codebase is well-structured, documented, and serves as a reusable foundation for your own agentic AI engineering projects.

Safety is treated as an architectural first principle throughout this course, not an afterthought. Every component is designed with safety in mind: semantic blueprints define agent scope boundaries, MCP tool schemas enforce action authorisation, input safeguards detect injection attempts before agent processing, output moderation validates responses before delivery, and the Glass-Box logging layer makes all safety-relevant decisions auditable. Safety architecture is integrated into every module rather than addressed in a single isolated section.

This course is designed for engineers who have used LLMs and built AI applications but want to move to production-grade agentic systems. You should be comfortable with Python and API calls, and ideally have some experience with multi-step AI workflows. Complete beginners to AI would find the course moves too fast; experienced prompt engineers will find it opens up an entirely new level of architectural capability.

Denis Rothman draws on decades of AI systems experience throughout the course: he knows why certain architectural patterns fail in production because he has seen them fail, he knows which safeguards are truly necessary versus theoretical concerns, and he knows how to explain complex engineering concepts clearly because he has spent three decades teaching them. His experience is embedded in every architectural decision in the Glass-Box Context Engine design.

The books provide comprehensive conceptual coverage. This live course provides hands-on implementation: you build a working agentic AI system in Python during the session, can ask Denis questions specific to your use case, and get immediate feedback when your implementation has issues. Many participants read the books first and attend this course to solidify their understanding through building. The course and books are complementary rather than redundant.