Roadmap
From zero to agentic AI — one flight path
Twelve stages, one winding road. Follow the plane from your first autonomous agent all the way to real-world deployments. No prior experience — and no coding background — required.
- Step 1
Introduction to Agentic AI
- AI with autonomous decision-making
- Difference between AI and agents
- Core agent capabilities and functions
- Applications in real-world automation
- Step 2
Gemini CLI and Agentic AI using Gemini
- Introduction to Gemini CLI for agentic development
- Working inside Visual Studio Code
- Building agents with the Gemini CLI
- Antigravity CLI for agentic development
- Examples of building agents with Gemini and Antigravity
- Step 3
Introduction to Claude
- How Claude was born and the evolution of agentic skills
- Installing local LLMs
- Benchmarking and testing local LLMs
- Project presentations and coding-with-Claude best practices
- Step 4
Extending Skills with MCP, Automation & Orchestration
- Using MCP with agentic AI to extend skills
- Orchestrating agent interactions
- Orchestrating multiple agents for complex tasks
- Automating workflows with agentic AI and MCP
- Step 5
Enterprise Agentic AI Skills (Business & SAP)
- SAP's AI roadmap and future plans
- SAP Joule and SAP's agentic AI capabilities
- Integrating SAP's agentic AI into business processes
- SAP agentic AI use cases and success stories
- Step 6
Voice Agents & Real-Time Actions
- Benefits and challenges of voice agents
- Voice-agent use cases and applications
- Interactive voice and memory agents
- Semantic search and document chunking for voice agents
- Step 7
The New Developer Experience with Agentic AI
- The evolving role of developers in the age of agentic AI
- New tools and platforms for agentic development
- Best practices for building and deploying agentic systems
- Vibe-coding best practices and developer experience
- Step 8
Knowledge Systems & Expert Masterclass
- Building knowledge systems with agentic AI
- Expert systems and their applications
- Integrating expert knowledge into agentic AI
- Masterclass on building expert systems
- Step 9
Reinforcement Learning & Self-Improvement
- Reinforcement learning with human feedback
- Adaptive learning in AI models
- Training agents with reward mechanisms
- Fine-tuning for specific problem-solving
- Step 10
Retrieval-Augmented Generation (RAG)
- Combining search with language models
- Enhancing AI memory through retrieval
- Hybrid AI for context expansion
- Using embeddings for knowledge retrieval
- Step 11
Deploying AI Agents
- Scaling AI applications using the cloud
- Deploying AI models through APIs
- Optimizing agents for low latency
- Monitoring and maintaining agent performance
- Step 12
Real-World AI Agent Applications
- AI-powered automation for businesses
- Autonomous research and data processing
- Enhancing workflows with smart agents
- AI assistants for decision-making support
The planned course arc — stages and detail evolve as the program grows.
Ready to take off?
This is the map. The cohort is the journey — live lessons, real projects and mentoring the whole way. Start with a question or grab the syllabus.