An AI agent framework handles some of the plumbing involved in connecting a model to tools and multi-step execution. Depending on the framework, it may provide tool definitions, state management, retries, tracing, human approval steps or multi-agent coordination.
The questions that matter are practical ones. Do you need built-in tracing? How is state stored between steps? Are tools defined with a tool schema? Can you swap model providers without rewriting the app? A team building one support agent may need only an agent loop and three tools, while a team coordinating several agents with approvals and retries may gain real value from a framework. Read the code of the parts you depend on, because behavior can change between versions.
A framework can speed up development, but it also introduces conventions and potential lock-in. Compare it with a small hand-written loop before adopting it; a simple task may not need a framework. Related: AI Agent, Tool Calling, Workflow Automation.