In This Handbook
Agentic AI Handbook
Only a small fraction of AI practitioners can confidently design, build, and reason about agentic systems in production. The most common pitfall isn’t knowing what agents are; it’s understanding how autonomous components think, plan, act, and coordinate under real constraints.
When it comes to Agentic AI, it’s not enough to prompt an LLM or wire together tools. Agentic systems introduce new challenges: planning vs. execution, memory management, tool reliability, safety boundaries, and multi-agent coordination. There are many valid designs for an agentic system—but only some communicate clear reasoning, robustness, and production readiness.
In this handbook, you’ll learn how to design and build agentic AI systems end-to-end, guided by practical frameworks used in modern AI products. Whether you’re a software engineer, ML engineer, or product builder, this handbook will equip you with actionable patterns, mental models, and design techniques to confidently build and evaluate agentic systems.
Takeaway skills
- Agentic System Design
- Agentic Design Patterns
What You’ll Learn
- Understand what Agentic AI is, and how it differs from simple LLM workflows.
- Know how to build a functional AI agent that can plan, act, and adapt.
- Apply Agentic RAG to extend agents with reliable external knowledge.
- Use proven agentic design patterns for tool use, memory, and control flow.
- Design end-to-end agentic systems with scalability and safety in mind.
- Reason about agent protocols and structured agent communication.
- Integrate MCP for standardized tool and model interaction.
- Know about agent-to-agent (A2A) collaboration for multi-agent systems.
Handbook Content
Agentic AI
This chapter introduces agentic AI fundamentals, focusing on how autonomous agents think, plan, act, and collaborate in real systems.
Agentic Protocols
This chapter focuses on how agents communicate, coordinate, and integrate with tools and other agents using structured protocols.
I’m Fahim, a software engineer specializing in generative AI, system design, and responsible AI adoption.
Generative AI
- What Is Generative AI?
- What are large language models
- Prompting: How we steer LLMs
- How are LLMs trained?
- Fine-tuning Large Language Models
- Why Language Models Need Retrieval
- What Is Retrieval-Augmented Generation (RAG)?
- Vector Databases and Semantic Retrieval
- Memory in Generative AI Systems
- Advanced RAG: When Naive Retrieval Breaks
- Graph-Based Retrieval and Structured Knowledge
- Generative AI System Design From Models To Systems
- Chatbots as Generative AI Systems
- What Is Multimodal AI?
- What is an AI Image Generator?
- What is an AI Video Generation?
- What is an AI Audio Generator?
- Applying Multimodal AI: Real-World Scenarios