Regardless of the development approach, all AI agents share key components: 

• Perception (Input): The ability to receive information from the environment (e.g., user prompts, databases, webhooks).
• Brain (LLM): The core reasoning engine that interprets input, formulates a plan, and decides on actions.
• Memory: The capacity to recall past interactions or access external knowledge bases (like company documents or databases) to provide context and maintain state (often using Retrieval-Augmented Generation, or RAG).
• Tools (Actions): Functions or APIs the agent can call to interact with external systems (e.g., searching the internet, sending emails, updating databases).
• Decision Making (Control Loop): The logic that orchestrates the flow: perceiving input, deciding on a sequence of actions, executing them, and generating a response. 

Development Approaches

You can choose from a range of approaches based on your technical expertise and needs: 

Development methods for AI agents vary in control and ease of use: 

• No-Code/Low-Code Platforms: Use visual tools for fast prototyping, though flexibility is limited.
• Frameworks (Python, TypeScript): Offer a balance of flexibility and speed with structured tools, but require coding skills.
• Building From Scratch: Provides maximum control but demands significant technical expertise and time. 

General Steps to Develop an AI Agent

A typical development process includes defining objectives, selecting technology, designing architecture, integrating tools and knowledge, developing logic and memory, testing, and deploying. 

For further learning, resources like the Microsoft Learn platform and freeCodeCamp are available.