Agentic AI: Gemini CLI and MCPs
FREE
Upcoming cohort
Nov 5, 2025Meeting time
Wednesday, 12:00PM ET
What will I learn?
- Explain how AI agents differ from standalone LLMs
- Apply the ReAct loop to design reasoning-and-action workflows
- Connect an AI agent to external data sources and tools using MCP
- Define tools and system prompts to extend an agent’s capabilities
- Build and run a basic agent using Gemini CLI
- Enhance your agent with memory, safety measures, and domain-specific knowledge
Curriculum
Introduction to AI Agents
The Model Context Protocol (MCP)
Designing Prompts and Tools
Building Your First Agent
Enhancing Agent Capabilities
Why Edlitera?
Build the coding, data and AI skills you need, online, on your own schedule. From learning to code as a beginner to mastering cutting-edge data science, machine learning and AI techniques.
Learning for the real world
Our courses are made with the input and feedback of top teams at Fortune 500 companies in Silicon Valley and on Wall Street.
No-fluff learning
Each minute of each course is packed full of insight, best practices and real-world experience from our expert instructors.
Learn by doing
Start writing code on your computer from Day One. Practice on hundreds of exercises. Apply your skills in mini-projects. Get instant feedback from video solutions.
Complete learning tracks
With over 150 hours of video lectures and hundreds of practice exercises and projects, our learning tracks will help you level up your skills whether you are a novice or an advanced learner.
What people are saying
"I walked into the bootcamp with some basic Python syntax and walked out with a much stronger, contextualized grasp of Python, an understanding of common mistakes, the ability to solve basic coding problems, and confidence in my ability to learn more."
Randi S., Edlitera Student
"I wanted to learn Python and be able to process data without being tied and limited by Excel and macros. These classes gave me all the tools to do so and beyond. The materials provided, the engagement of the class by the tutors and their availability to help us were excellent."
Gaston G., Edlitera Student
Course Syllabus
1. AI Agents vs. LLMs
- LLMs as text generators vs. agents as autonomous systems
- How agents use planning and tools to solve multi-step tasks
- The ReAct cycle: think → act → observe → repeat
2. Agent Architecture
- Core components: agent core, planning module, tools, and memory
- Planning techniques: task decomposition and reflection
- Short-term vs. long-term memory modules and retrieval strategies
3. The Model Context Protocol (MCP)
- Why MCP matters: standardized, scalable integration with tools
- Key components: host process, client instances, MCP servers
- Typical workflow: handshake, capability discovery, tool invocation
- Example: connecting Gemini CLI to GitHub, filesystem, and memory MCP servers
4. Working with Gemini CLI
- Installing and setting up Gemini CLI with Node.js
- Logging in and accessing Gemini models
- Using built-in tools (Google Search, ReadFile, WebFetch, etc.)
- Connecting Gemini CLI to VSCode
5. Building an End-to-End Agent
- Creating a project and configuring MCP servers
- Running complex multi-step prompts with Gemini CLI
- Example: generating onboarding documentation for developers using GitHub repos
6. Enhancing Your Agent
- Adding domain-specific knowledge and memory persistence
- Using safety measures: restricting tool access and validating outputs
- Real-world use cases: onboarding, research automation, workflow assistants
Have a question?
Contact us any time, we'd love to hear from you!