How to Build Your First AI Agent with Claude and the Anthropic API

The fastest way to understand agentic AI is to build a small one. A good first project is a research agent: give Claude a `search_web` tool and a `read_page` tool, describe each one clearly (what it does, what input it expects, what it returns), and give the model a goal like "research X and produce a short summary with sources." On each turn, the model either calls a tool or returns a final answer; your code executes whichever tool was requested, feeds the result back, and lets the model continue — that loop is the entire "agent."

The details that make the difference between a toy and something useful are mostly about guardrails: cap the number of tool calls so a confused agent can't loop forever, return clear error messages when a tool fails so the model can adjust rather than retry the same broken call, and log every tool call and result so you can debug why the agent did what it did. Once that loop works reliably for a narrow task, the same pattern extends to far more capable agents — the core mechanics (describe tools, execute what's requested, feed back results, repeat) don't change much as the toolset and task complexity grow.