What Are AI Agents?
An AI agent is a system that can perceive its environment, make decisions, and take actions to achieve goals — autonomously. Unlike chatbots that respond to individual prompts, agents maintain state, use tools, and execute multi-step workflows.
The Building Blocks
Every AI agent consists of:
- Planning: Breaking complex goals into actionable steps
- Memory: Maintaining context across interactions (short-term and long-term)
- Tool Use: Calling APIs, executing code, browsing the web
- Reflection: Evaluating outputs and self-correcting
Popular Agent Frameworks
The developer ecosystem has converged around several frameworks:
- LangGraph: State machine approach for complex agent workflows
- CrewAI: Multi-agent collaboration framework
- Claude Agent SDK: Anthropic's official toolkit for building Claude-powered agents
- AutoGen: Microsoft's multi-agent conversation framework
When to Use Agents vs. Simple Prompts
Not every AI application needs to be an agent. Use agents when your task requires multiple steps, tool use, or adaptive decision-making. For simple question-answering or text generation, a well-crafted prompt with RAG is usually sufficient and more predictable.
Production Challenges
Deploying agents in production comes with unique challenges: unpredictable execution paths, cost management (agents can make many LLM calls), error handling for multi-step failures, and observability. Tools like LangSmith and Braintrust are essential for monitoring agent behavior.