Agents, powered by large language models (LLMs), are independent digital assistants that can perform tasks using LLMs for reasoning and decision-making, and external tools for information gathering and action. They excel at complex decision-making, difficult-to-maintain rules, and unstructured data.

A guide to building AI agents suggests starting with a high-performing model to establish a baseline, then experimenting with smaller, faster models for cost and speed optimization. It outlines three foundational components: models, tools, and instructions. Standardizing tools for reusability and manageability is crucial, and best practices for writing effective instructions, including leveraging existing documentation and anticipating edge cases, are provided.

Agents, powered by LLMs and external tools, automate complex workflows within safety boundaries set by guardrails, which can be LLM-based, rules-based, or external moderation APIs. Human oversight is crucial, especially during initial deployment, to catch unexpected failures and continuously refine the agent’s performance.