The practical question is where to spend an inference budget. Instead of increasing model size or retraining, a system can give a difficult request more attempts, more steps, or more verification. That makes the method useful for problems where a fast first answer is less valuable than a checked one.
What counts as test-time compute?
A reasoning model may generate additional internal steps. A system can also sample multiple answers, run code or retrieval tools, and use an LLM as a judge or deterministic check to choose one. The defining feature is extra work during the request, not the specific algorithm.
What is the production trade-off?
More compute can improve a measurable quality metric, but it raises cost and latency and may still fail on the same missing knowledge. Budget it by task difficulty and evidence, not by a blanket “think longer” setting. Extra inference is useful only when the evaluation shows a quality gain worth its cost. AI agent evals should compare the added work against a baseline on real tasks. For a simple extraction or routing job, ordinary inference is often the better choice.