Choosing an AI-agent framework should begin with the workflow, not the framework’s popularity. A simple request-and-response assistant may need only a model SDK and a few typed functions. A multi-step process may require durable state, retries, approvals, tracing, and a way to replay failures.
Evaluate alternatives across several dimensions. Can the framework express the workflow clearly? Are tool permissions explicit? Can the team inspect intermediate decisions? Does it support structured outputs, timeouts, cancellation, and human review? How easy is it to test without making live model calls? These questions reveal operational quality better than a feature list.
Run a proof of concept using one real workflow and record latency, cost, failure modes, and developer effort. Keep business logic separate from framework-specific code so migration remains possible. The right alternative is the one that makes the required behavior easier to control, test, and maintain – not necessarily the one with the most abstractions.