The 5 Day AI Agents Intensive is a hands on learning program created by Google researchers and engineers. It is designed to help developers understand the foundations of AI agents and learn how to build production ready agentic systems. The course covers core components such as models, tools, orchestration, memory and evaluation. It also shows how agents evolve from simple LLM prototypes into reliable systems that can run in real world environments.
Table of contents
Day 1: Introduction to Agents
The Day 1 whitepaper introduces the basics of AI agents. It explains different agent capabilities and the need for Agent Ops for reliability and governance. It highlights the importance of identity and policy constraints for safety.
What learners will learn?
- What AI agents are
- How agents differ from normal LLM prompts
- Core agent capabilities
- The role of Agent Ops
- Why identity, policies and security matter
- How to build a simple agent using ADK and Gemini
Click here to access the Google research paper on basics of AI agents!
Day 2: Agent Tools and Interoperability with MCP
The whitepaper explores the use of external tools. It explains how tools help an agent access real time data and perform actions. It also introduces the Model Context Protocol. The paper covers MCP architecture, communication layers, and enterprise readiness gaps.
What learners will learn?
- How agents use tools to take actions
- How to convert Python functions into agent tools
- How Model Context Protocol works
- How MCP supports interoperability
- How to design safe and effective tools
- How to build agents that wait for human approval
- How long running tool calls work
Click here to access the Google research paper on Agent Tools!
Day 3: Context Engineering, Sessions and Memory
The Day 3 whitepaper explains context engineering. It describes sessions as short term conversation history and memory as long term stored information. The focus is on building agents that stay consistent across multiple interactions.
What will you learn?
- How agents manage contextual information
- How sessions store short term conversation history
- How memory stores long term knowledge
- How context engineering improves multi turn conversations
- How to give agents persistent memory across sessions
- How context windows are structured
- How to design more personalized agent experiences
Click here to access the Google research paper on Context Engineering and Memory!
Day 4: Agent Quality
This whitepaper focuses on evaluation and quality assurance. It introduces logs, traces and metrics as the three pillars of observability. Also, the paper explains how these signals help developers understand agent behavior. It also covers scalable evaluation methods such as LLM as a Judge and Human in the Loop testing.
What will you learn?
- How to measure agent reliability
- What logs, traces and metrics mean
- How to debug agent behavior
- How to analyze tool use
- How to evaluate responses with LLM as a Judge
- How to include human evaluation
- How to monitor agent performance across time
Click here to access the Google research paper on Agent Quality!
Day 5: Prototype to Production
The final whitepaper describes the operational lifecycle of AI agents. It covers deployment, scaling and the shift from prototypes to enterprise solutions. It explains the Agent2Agent Protocol and how it enables communication among independent agents.
What will you learn?
- How to take agents from prototype to production
- How deployment pipelines work
- How to scale agents in real environments
- How the Agent2Agent Protocol works
- How agents collaborate at scale
- How to deploy agents using Vertex AI Agent Engine
- How to structure enterprise agent systems
Click here to access the Google research paper on Prototype to Production!
You can find all about the Google’s Free course on AI Agents here.
Other Helpful Resources to Learn Agentic AI
- Agenti AI Pioneer Program: A 150-hour immersive program offering 50+ real-world projects and 1:1 mentorship. Designed to take you from beginner steps to building autonomous AI agents across tools like LangChain, CrewAI and more.
- AI Agent Learning Path: Structured as a curated learning path, this course helps you build and deploy agentic systems by covering core components, orchestration and evaluation through hands-on labs and guided study modules.
- Building a Multi-agent System: Focused on multi-agent architectures, this course uses LangGraph to show you how to design collaborating agents, handle tool calls, and integrate memory and context to support complex workflows.
- Foundations of MCP: This deep dive explains the MCP framework, detailing how agents use external tools and context to act intelligently, including best practices for tool design and managing long-running operations.
Conclusion
Learning AI agents is easier than ever with the right guidance. Google’s 5 Day AI Agents Intensive gives developers a complete foundation in agent architecture, tools, memory, evaluation and production deployment. And if you want mentorship, hands-on projects and a clear roadmap to build a career in agentic AI, our Agenti AI Pioneer Program is the best place to start. The course covers hands-on projects, expert support and all the things you need to build a career in the field.
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