What is ai?
Production AI is the operation of model-based features in a real application. It includes model inference, application logic, data access, evaluation, monitoring, and the people responsible for outcomes.
o10Reviewed 2026-09-06
Production AI: models, inference and agents
Production AI is the operation of model-based features in a real application. It includes model inference, application logic, data access, evaluation, monitoring, and the people responsible for outcomes.
SummaryKey takeaways
What you need to know
Start with the core questions, then examine the examples and tradeoffs below.
01Deep dive
Start with the user task
Describe the result the application must deliver before choosing a model. A classification system, a document search tool, and an action-taking agent need different input contracts, checks, and recovery paths. Success should be defined in terms of the user task, not the fluency of generated text.
An AI feature can be useful without being autonomous. Keep reliable deterministic steps in code and introduce model decisions where the input cannot be handled adequately by simpler logic.
02Deep dive
Separate the model from the system
The application prepares context, controls access, validates results, and handles errors around each inference request. Retrieval supplies information; a model generates or scores an output; tools act on external state. A failure in any stage can affect the final result.
Assign ownership for data, tool permissions, model configuration, and operational incidents. A vendor benchmark cannot demonstrate the reliability of this full system.
03Deep dive
Release against measurable acceptance criteria
Compare a new configuration with a baseline on representative examples. Record rejected answers, latency, escalations, and total cost. Include a rollback path and repeat the comparison when prompts, data, tools, or model versions change.
Use the AI inference guide for serving and cost decisions, and the AI agents guide when a task needs a sequence of model-selected actions. o10 focuses on inference routing; application-level authorization and outcome verification still belong to the system using it.
SourceMethodology
Cost examples are illustrative. Measure quality and total costs on your own tasks, and check current endpoint terms before implementation.
FAQFrequently asked questions
Common questions
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See what you're overpaying.
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See what you're overpaying →verified savings methodology · State of Inference Spend 2026