AI Agents — How They Work

What Is an AI Agent?

AI agents go beyond chatbots by perceiving their environment, reasoning about what to do, and taking real-world actions — making them the dominant pattern for production AI deployments.

Core Components of an AI Agent

Every AI agent is built from five core components: a perception module, memory systems, planning and reasoning, tool use, and action output — understanding each is essential for building or evaluating agents.

Model Context Protocol (MCP)

MCP is the open standard that lets any AI agent connect to any tool or data source — ending the fragmented ecosystem of one-off integrations and enabling the truly composable AI stack.

Agentic Frameworks & Memory Systems

A practical guide to the frameworks, patterns, and orchestration models developers use to build production AI agents — from the dominant ReAct pattern to multi-agent architectures and open-source frameworks.

Challenges and Safety in Agentic AI

Agentic AI introduces failure modes that don't exist in single-turn LLM interactions — from error compounding and prompt injection to the cost of autonomous action — and getting safety design right is what separates working production agents from dangerous ones.