Understand
what’s next.

An AI agent uses a model to decide what to do next. It can choose a tool, inspect the result and use that feedback to continue towards a goal.

A fixed workflow follows steps defined in advance. An agent has more freedom to choose the path. That flexibility can help with open-ended tasks, but also creates more opportunities for mistakes.

The useful question: does this task need flexible decisions, or would a predictable workflow do it better? Clear permissions, stopping conditions and human review help keep the work controlled.

During training, a model’s internal parameters are adjusted using examples. This is where it learns patterns that can help it handle new inputs.

Inference is the use of a trained model to produce an output: a response, a prediction or an image. Sending a prompt is usually an inference step; it does not itself retrain the model.

Why it matters: the computing needs of building a model and serving it to people are different. Both matter when comparing speed, cost and the infrastructure behind an AI product.

A benchmark measures performance on a defined set of tasks under particular conditions. It is evidence about those tasks, rather than a complete picture of a model’s usefulness.

Look at what was tested, how it was scored and whether the conditions match the job you need done. Reliability, speed and cost can matter as much as the headline result.

A better comparison: try representative examples from your own work, keep the conditions consistent and inspect the failures as carefully as the successes.