Full guide to building AI agents: Understanding the core components and workflows

Building AI agents doesn't have to be overwhelming, especially if you understand the fundamental components and common workflows. Let's break down this comprehensive guide to help you get started, whether you're a non-coder or an experienced software engineer.

What are AI agents?

AI agents are systems that perceive their environment, process information, and autonomously take actions to achieve specific goals. From a human perspective, they often serve as AI counterparts to human roles or tasks – like coding assistants or customer service chatbots.

The most effective AI agents aren't single entities trying to do everything, but rather consist of specialized sub-agents working together – similar to how companies have employees with different roles.

The essential components of AI agents

Every functional AI agent requires these key components:

1. Models – The core intelligence that powers reasoning and decision making

   • Options include GPT models (4o, 4.5), Claude Sonnet 3.7, Gemini 2.5 Pro, or open-source models
   • Choose based on reasoning abilities, speed, cost, and context window requirements

2. Tools – Interfaces that allow the agent to interact with the world

   • Web search, email access, calendar integration, file system access
   • Can be implemented via OpenAI's Agent SDK, MCP (Model Context Protocol), or no-code platforms

3. Knowledge and memory – Information storage for the agent

   • Static knowledge bases (documents, policies)
   • Persistent memory to track conversation history and user preferences

4. Audio and speech – Natural language interaction capabilities

   • Voice input/output functionality
   • Transcription services like Whisper

5. Guardrails – Constraints to prevent harmful or irrelevant behaviors

   • Ensures the agent stays on task and follows guidelines

6. Orchestration – Management of agent deployment, monitoring, and improvement

   • Handles how different sub-agents work together
   • Manages ongoing maintenance and updates

Common agent workflow patterns

How you structure your AI agent's workflow depends on the complexity of your task:

1. Prompt chaining – Breaking tasks into sequential steps where each sub-agent processes the output of the