// learn · agents
Agentic AI
Agentic AI is where models stop answering and start acting — planning, using tools, and iterating toward a goal. This is the path to building agents that are useful and safe in production.
New to the term? Start with the definition of agentic AI in the glossary.
How an agent acts — invoking defined functions with structured arguments.
Short- and long-term context so the agent remembers across steps.
Deciding the next action, looping, and knowing when to stop.
Constraining inputs/outputs and measuring behavior before you ship.
- 01What is agentic AI? A developer's guideThe concepts, in plain developer terms.
- 02How to build an AI agent from scratchThe agent loop, tools, and memory — with code.
- 03LangChain vs LlamaIndexDo you even need a framework? When to reach for which.
- 04Production-ready GenAI architectureThe layers that keep an agent reliable in production.
- 05AI engineer interview questionsAgent and system-design questions you'll be asked.
Frequently asked questions
What is an AI agent?
An AI agent uses an LLM to decide actions, call tools or APIs, observe the results, and iterate toward a goal — rather than answering in a single shot. Production agents add memory, guardrails, retries, and human-in-the-loop control.
How is an agent different from RAG?
RAG retrieves context to answer one question. An agent takes multiple steps and uses tools to accomplish a task — it may use RAG as one of its tools. Agents are more capable but harder to evaluate and control.
Do I need a framework like LangChain?
Not to start. Building a simple agent loop yourself teaches you the mechanics. Reach for a framework when you need integrations, tool abstractions, or workflow orchestration — see our LangChain vs LlamaIndex comparison.
How do you keep agents safe?
Give tools least privilege, validate and isolate untrusted input (defend against prompt injection), constrain outputs with schemas, add human approval for risky actions, and gate releases behind evals.
Production AI Notes
One practical AI engineering email each week
One concept, one architecture, one project idea, and one interview question — written for developers who want to build and ship real AI systems.