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Comprehensive guide to AI agents in 2025
- This comprehensive guide explores AI agents, their core components, and how to evaluate and improve them, frameworks, best practices, and tips for building AI agents and designing product user experiences with AI agents.
- AI agents can plan and achieve goals by accessing external information or using tools autonomously; they can perform complex tasks on their own.
- However, AI agents may not be necessary for simple, well-defined tasks and flows to replace existing programmed functions and workflows.
- There are also potential risks when using AI agents, such as privacy and ethical concerns.
- Components of AI agents include LLM or VLM for brains, external tools, memory for context, and glue code to link them together.
- AI agents work through a continuous ReAct loop, reasoning-acting, to achieve a given goal with the tools and information available.
- Planning helps agents stay focused on their goals, be more efficient during execution steps, recover from errors when a step goes wrong, collaborate & cooperate in a multi-agent setting, and agents can make better plans with task decomposition, multi-plan selections, external planners, and external info.
- RAG, retrieval-augmented generation, provides external knowledge for AI agents, while function-calling and APIs allow them to interact with the external world.
- Frameworks for building AI agents include OpenAI's Swarm, PydanticAI, Google's Vertex AI Agent Builder, Amazon's Bedrock Agents, LangChain, LangGraph, and more.
- When designing product experiences with AI agents, you can think about them on three levels: user-triggered, event-triggered, and predictive.
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