AI inference and agent use cases
Choose a workflow. Define what success means. Measure quality, latency, and cost before changing the route.
These guides cover workflow design, evaluation, and rollout. Use your own evaluation results to assess quality and cost.
A support assistant retrieves account or product information and drafts responses to customer questions. The useful unit is a correctly reso…
Retrieval-augmented generation (RAG) supplies retrieved evidence to a model before it generates an answer. A successful response must use re…
Code assistants propose, explain, or modify software. Their inference cost should be assessed alongside whether the change satisfies the tas…
Batch classification assigns labels to a collection of records outside an interactive request. Throughput and completion deadlines matter al…
AI can help organize evidence for fraud reviewers or classify signals for further investigation. Model output should be evaluated against th…
Clinical document summarization condenses records for qualified reviewers. A readable summary alone does not establish clinical accuracy or …
Knowledge search retrieves information from a corpus in response to a query. Embedding, keyword retrieval, reranking, and optional answer ge…
An AI agent uses model outputs to choose actions, call tools, and update its plan. The relevant outcome is a completed task within its permi…
Real-time classification assigns a label while an application is waiting for a response. The design must meet both the decision-quality requ…
Document summarization turns source material into a shorter representation for a defined reader and purpose. Quality depends on what is reta…
Evaluate each workflow against your own quality, latency, and cost requirements.
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