AI Agents: A Practical Guide
From Concepts to Production
By Deepak Gupta3 hours12 chaptersPublished Updated
A practical guide to understanding, building, and deploying AI agents. Covers architecture patterns, tool use, memory systems, multi-agent coordination, RAG, safety guardrails, MCP and A2A, and real-world use cases, with working code examples throughout. Updated November 2026.
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What Are AI Agents?
Understanding autonomous AI systems, the agent loop, and how agents differ from chatbots and copilots.
Types of AI Agents
From reactive to learning agents: a complete taxonomy of agent architectures with real-world examples.
Building Your First Agent
A hands-on walkthrough of creating a functional research agent from scratch with working code.
Tool Use and Function Calling
How agents interact with external systems through tools, APIs, and function calling across LLM providers.
Memory and Context Management
Building memory systems that let agents maintain state across conversations and sessions.
Planning and Reasoning Patterns
ReAct, Plan-and-Execute, Tree of Thoughts, Reflexion, and other patterns for agent reasoning.
Multi-Agent Systems
Designing systems where multiple specialized agents collaborate to solve complex problems.
Retrieval-Augmented Generation for Agents
Building RAG pipelines that give agents access to external knowledge with practical implementation guides.
Agent Guardrails and Safety
Input validation, output checking, prompt injection defense, cost control, and compliance for autonomous agents.
Deploying Agents to Production
Architecture, containerization, scaling, observability, and CI/CD for production agent systems.
Real-World Use Cases and Patterns
Eight use-case patterns across engineering, support, data, sales, security, DevOps, research, and productivity.
The Future of AI Agents
Emerging capabilities, MCP and A2A, computer use agents, economic impact, and what to build today for tomorrow.