Agentic AI is moving fast from hype to hands on reality. But most teams still struggle with the same questions:

- How do I prompt agents effectively?

- How do I control context and tools?

- And how do I use agentic AI in real brownfield and greenfield projects without creating chaos?

In this full day masterclass, we start by grounding participants in what they actually need to know about agentic AI. We cover practical prompting strategies, context engineering, and MCP, with a clear focus on what works in real projects. No buzzwords. No magic thinking.Bring your own project and your own agentic AI setup. Participants can work on their own ideas or systems, or follow along with provided examples. I will use examples with GitHub and Azure as this is my personally preferred tech stack, but the learnings and patterns apply everywhere. From there, we go fully hands on. Using spec kit, participants work through a choose your own adventure style journey. You decide whether you start from a greenfield idea or a brownfield application. Step by step, we turn specifications into working code and cloud ready artifacts using agentic workflows. This is not a slide heavy workshop. You will write specs, guide agents, debug outcomes, and learn where agentic AI shines and where it clearly does not. You will leave with concrete patterns and mental models you can apply immediately in your own projects.

No prior experience with agentic AI is required.

Basic software development experience is highly recommended.

Key learnings:

- Understand how agentic AI actually works in practice and where its limits are.

- Learn how to write effective prompts that guide agents instead of fighting them.

- Understand context engineering and how MCP fits into real workflows.

- Use specifications as a stable interface between humans and AI agents.

- Apply agentic workflows to both brownfield and greenfield projects.

- Turn specs into code and cloud ready artifacts.

- Recognize common failure modes and how to debug agent driven development.

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