Build AI Agents

AI agents go beyond prompts — they plan multi-step tasks, use tools, observe results, and decide next actions. ChatGPT is a conversation partner. An AI agent is a worker that takes actions in the world.

Function calling lets LLMs output structured commands. Define tools as JSON schemas. LLM decides which tool to use. Code executes and feeds results back. Examples: search, database, file ops, email, calculations.

ReAct: Reason → Act → Observe → Repeat. Plan-and-Execute: create plan first, then execute. Reflection: review work and improve. These loops make agents powerful — they don't just answer, they DO.

LangChain: framework with agents, tools, memory, chains. CrewAI: multi-agent teams where specialized agents collaborate. Start single-agent, then scale to multi-agent with roles like researcher, writer, reviewer.

Short-term: conversation history. Long-term: vector store of past interactions. State management: track progress in multi-step tasks. Persistence: save state to resume after interruptions.

Production: rate limiting, cost tracking, error recovery, human-in-the-loop. Monitoring: log tool calls, track tokens, set alerts. Safety: guardrails, validate outputs, restrict access. Start with human approval, then graduate to autonomous.