Learn agentic AI
Free, practical guides for students and engineers who want to understand, build, and ship AI agents. Start from first principles and work your way to production-grade autonomous systems — no fluff, no paywall.
- 21 in-depth guides
- Beginner to advanced
- Updated 2026
This is the learning hub for everyone who wants to learn agentic AI — the field of building software that reasons about a goal, takes real actions with tools, and keeps going until the work is done. Each guide is written to be genuinely useful: clear definitions, concrete examples, real concepts like ReAct, tool calling, RAG, vector stores, and orchestration — and zero filler.
The curriculum moves in a deliberate order. You'll start with what an AI agent actually is and how it differs from the generative AI you already know. From there you'll compare frameworks, build your first agent, give it tools and memory, and finally orchestrate multi-agent systems ready for production.
Who it's for: students exploring the topic for a course or project, software engineers adding agents to a product, and technical leaders who need an accurate mental model before committing a roadmap. If you can read code and call an API, you have everything you need to begin.
Every agentic AI guide, in one place
Twenty-one focused lessons covering the full stack of building AI agents — from fundamentals to RAG, evaluation, security, and deployment. Read them in order, or jump straight to what you need.
What is agentic AI?
Start hereThe fundamentals: agency, the perceive–reason–act–observe loop, and the five components of every agent.
Learn moreAgentic AI vs generative AI
Why generative AI answers and agentic AI acts — and how one contains the other. Clear up the confusion fast.
Learn moreLLM agents explained
How large language models become agents through reasoning, tool calling, and the ReAct pattern.
Learn moreHow to build AI agents
A practical, step-by-step build: from a single prompt to a working autonomous agent that uses tools.
Learn moreAI agent frameworks
Compare the leading frameworks and SDKs so you can choose the right stack for your project.
Learn moreAI agent tools
Function calling, APIs, and the tool patterns that let an agent take real actions in the world.
Learn moreAI agent memory
Short-term context vs. long-term memory, vector stores, and retrieval-augmented generation (RAG).
Learn moreAgentic workflows
Patterns like planning, reflection, routing, and evaluator–optimizer that make agents reliable.
Learn moreMulti-agent systems
Orchestrator–worker designs, delegation, and coordination for teams of specialist agents.
Learn moreRAG for AI agents
Retrieval-augmented generation: ground agent answers in your real, current data with retrieval.
Learn moreFunction calling
How LLMs emit structured tool calls from a JSON schema — the mechanism beneath every tool-using agent.
Learn moreVector databases
Embeddings, similarity search, and ANN indexes — the storage layer behind RAG and long-term memory.
Learn moreAI agent architecture
The canonical components — model, planner, tools, memory, orchestration, guardrails — and how they fit.
Learn moreAI agent orchestration
Routing, handoffs, and orchestrator–worker patterns for coordinating steps and multiple agents.
Learn moreAutonomous agents
What 'autonomous' really means, the levels of autonomy, and how to keep self-directed agents safe.
Learn moreAI agents vs chatbots
Pursuing goals with tools and actions versus answering one turn at a time — and when each is enough.
Learn morePrompt engineering
System prompts, tool descriptions, few-shot examples, and context engineering for reliable agents.
Learn moreAI agent evaluation
Measure task success, tool accuracy, and grounding — and run agent evals in CI to catch regressions.
Learn moreAI agent observability
Traces, spans, token and cost signals — see exactly what an agent did, and debug it with confidence.
Learn moreDeploying AI agents
Take agents to production: hosting, state, concurrency, rollouts, and human-in-the-loop checkpoints.
Learn moreAI agent security
Prompt injection, least privilege, sandboxing, and the layered guardrails that keep agents safe.
Learn moreA six-step path from zero to production
Follow these stages in order. Each one maps to a guide and builds directly on the last.
Understand the fundamentals
Learn what agentic AI is, how the agent loop works, and how it differs from generative AI. Build the mental model before writing any code. → 'What is agentic AI?'
Compare frameworks
Survey the agent frameworks and SDKs, weigh their trade-offs, and pick the stack that fits your language and use case. → 'AI agent frameworks'
Build your first agent
Wire an LLM into a reasoning loop and ship a minimal working agent that completes a real task end to end. → 'How to build AI agents'
Add tools & memory
Give the agent function-calling tools to act and a memory layer (with a vector store and RAG) so it remembers context. → 'AI agent tools' + 'AI agent memory'
Orchestrate multi-agent teams
Decompose hard goals across specialist agents with an orchestrator, and apply workflow patterns for reliability. → 'Multi-agent systems' + 'Agentic workflows'
Ship to production
Add evaluation, guardrails, observability, and SOC 2-aligned security, then deploy with confidence. → Explore the docs and SDKs.
Free guides
no paywall
Path stages
zero to prod
First prototype
is realistic
Hands-on
concepts + code
How to use this path
New to the topic? Go top to bottom. Already building? Jump to the stage you're stuck on — each guide stands on its own, with cross-links back to the fundamentals when you need them.
Core agentic AI concepts you'll master
The vocabulary of the field — every term below is covered across the guides above.
Learning agentic AI, answered
Start with the fundamentals: read 'What is agentic AI?' to understand the perceive–reason–act–observe loop, then compare it with generative AI so the boundary is clear. Once the mental model clicks, move into the hands-on guides — building your first agent, wiring up tools, and adding memory. Every guide here is free, self-contained, and ordered so you can follow it top to bottom without prerequisites piling up.
Put the guides into practice
When you're ready to build, these product resources take you from concept to a running agent.
Ready to build your first AI agent?
You've learned the theory — now ship the real thing. Start free, no credit card required, and go from guide to running agent in minutes.