A crash course in building real AI agents, from your first LLM call to a secured,
production-ready system, not just prompting a chatbot.
Anyone can send a prompt to an LLM. Few can turn that into a system that connects to real tools, reasons across multiple steps, and survives contact with production. That gap between an AI demo and an AI agent that ships is where this course lives.
This is a code-first program built for people who want to build the thing, not just talk about it. No slides-only theory. Every module closes with a hands-on project, and the course ends with a full enterprise capstone.
Developers
Already comfortable with Python. Ready to move from writing functions to building systems that reason and act.
Practitioners
Understand models and data. Want the engineering layer that turns that knowledge into deployed agents.
SysOps
Own infrastructure and systems. Ready to bring AI agents into production the way they already bring in any other service.
Every agentic system is built the same way, one layer at a time. Skip a layer and it breaks in production, at the worst moment.
Tell the AI exactly what to do, clearly and precisely.
Give AI the right information at the right time.
Give AI the ability to use real tools effectively
Let AI reason, act, observe results, and adjust its approach.
A structured path from how modern AI systems actually work to building and deploying production-grade AI applications, with live project work built into every stage. A working knowledge of Python is expected going in. If you’re starting from scratch, Meii’s Python Full Stack track is built to get you there first.
Get comfortable with NumPy, Pandas and REST APIs for AI workflows, then build your first LLM-powered application: one that turns a business requirement into structured risks, stakeholders and recommended actions.
Move past basic prompting into structured prompting, prompt chaining and context engineering. Then connect AI to real business knowledge through retrieval-augmented generation, vector search and grounded, source-cited answers.
Give AI the ability to act. Build function-calling tools, work with the Model Context Protocol, and build an AI Operations Assistant that can check orders, search customers and create support tickets on its own.
Learn what actually makes a system agentic: the reason-act-observe-iterate loop. Build a task-oriented agent using a modern framework, then scale into multi-agent systems with supervisor, planner-executor and researcher-reviewer architectures.
Give agents memory that persists across sessions. Automate real workflows with n8n and Power Automate. Then build the business case: KPIs, ROI, risk assessment, and the call on when not to automate at all.
Take an agent from a working prototype to a production-ready system. Cover cloud deployment, secrets management, monitoring, prompt injection defense, RBAC, and how to align an agentic AI program with ISO/IEC 42001.
Design and build a complete enterprise Agentic AI solution end to end, from business problem to final demonstration. Choose from cross-industry, UAE-focused or India-focused project tracks.
A Crash Course Curriculum Designed Around How Agents Actually Get Built
Every concept moves straight into a build. Nothing stays theoretical long enough to go stale.
Fifteen sections, seven real applications built along the way: from a business data assistant to a full multi-agent research team.
Get deep, hands-on experience with one modern agent framework while understanding the tradeoffs across the rest of the ecosystem, so the skill outlasts any one tool.
Prompt injection, data leakage, RBAC and ISO/IEC 42001 alignment are part of the core path, not an afterthought module.
Choose an industry track that matches where you’re headed, from IT service desks to UAE real estate operations to Indian BPO automation.
Your Learning Journey
Every module traces back to problems people have actually solved deploying AI inside live systems, not textbook scenarios.
Weekly reviews and feedback from mentors actively working in AI roles today, not full-time trainers.
Every concept moves straight into a real build. Nothing stays theoretical for long.
Clear goals, regular reviews, and iteration each week, not open-ended, self-paced learning.
Deployed systems, real project narratives, and work you can walk hiring teams through in an interview.
We look for curiosity and commitment, not a perfect resume.