agentlearn - Master AI Agent FundamentalsMaster AI agent fundamentals — learn patterns, protocols, and best practices for building intelligent agents.Chapters

What is an AI Agent? — The fundamental building blocks that turn a language model into something that takes action.
The LLM Core — How language models generate text and the parameters that control their behavior.
Context Engineering — The art of managing what the model sees at each step.
Prompting for Agents — How to instruct agents effectively through system prompts and examples.
Tools & Function Calling — How agents interact with the world through function calling.
Memory Systems — How agents remember things between steps and across conversations.
Error Handling & Guardrails — Making agents reliable and safe.
Agentic Patterns — The major patterns for organizing agent behavior.
Testing & Debugging Agents — How to test, debug, and fix agents.
Model Context Protocol (MCP) — The standard protocol for connecting agents to tools.
Agent-to-Agent (A2A) — Google protocol for agents communicating with each other.
Agentic Protocols — The standardized protocols for agent communication and tool access.
Agent Skills — A lightweight, open format for extending AI agent capabilities.
Multi-Agent Systems — Coordinating multiple agents to solve complex problems.
Scaling & Production — Challenges when taking agents from prototype to production.
RAG & Knowledge Retrieval — Retrieval-Augmented Generation for grounding agents in real data.
Agent Security & Red Teaming — Defending agents against prompt injection, jailbreaks, and data exfiltration.
Evaluations & Benchmarks — Measuring agent quality with systematic evaluation frameworks.
On-Device & Edge AI Agents — Running AI agents locally with Gemini Nano and small language models.
Agent Frameworks & Ecosystem — Comparing LangGraph, Google ADK, CrewAI, AutoGen, and more.
Observability & Monitoring — Tracking, logging, and monitoring agent behavior in production.
Cost Optimization — Reducing token usage and costs while maintaining quality.
Putting It Together — Building a complete agent from all the fundamentals.
Real-World Case Studies — How GitHub, Perplexity, and others built production agents.