smart_toyContent Hub · AI Agents

AI Agents

The central guide — from the basic concepts to building an agent that analyzes, decides and acts on its own: fundamentals, RAG, frameworks, MCP and security. Start here.

An AI agent isn't just a chat — it's a system that takes a goal, plans steps, uses tools (search, API, code), reads from your knowledge sources (RAG), and performs actions until the result is achieved. This page gathers everything you need to understand, build and secure a real AI agent — step by step.

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AI agent fundamentals

What an agent is, how it plans and acts, and how to connect tools and capabilities.

arrow_backAI agents guide — from the basics arrow_back10 agentic design patterns arrow_backMCP — connecting tools to an agent arrow_backComputer Use — an agent that operates a computer arrow_backA2A — agent-to-agent communication
database

Knowledge & context (RAG)

How to give an agent access to your information so it answers accurately, without making things up.

arrow_backRAG — answers from your data arrow_backAdvanced RAG — improving accuracy arrow_backVector Databases arrow_backLangChain
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Frameworks & building

The tools and libraries you actually build agents with — and ready-to-run templates.

arrow_backAgent Frameworks — comparison arrow_backBuilding an MCP server arrow_backCoding agents arrow_backCrewAI & RAG templates to download
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Security & reliability

How to keep the agent safe, predictable and measurable — before you ship to production.

arrow_backAI agent security arrow_backGuardrails — safety rails arrow_backLLM security arrow_backAI Observability — monitoring
hubAutomation Hub compare_arrowsWhich LLM for an agent widgetsTool library

Want to build an AI agent for your business?

We'll take you from idea to an agent running in production — with RAG, tools and security done right.

edit_noteSend a request smart_toyStart with the fundamentals