Production AI: models, inference and agents

Source page: https://www.o10.io/ai

Plain-text reference for reading, copying, and citation. Examples are illustrative; use your own credentials for API requests.

Model data: https://www.o10.io/api/models.json
Content index: https://www.o10.io/llms.txt

# 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.

## Key takeaways

### 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.

## 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.

## 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.

## 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.

## Methodology

Cost examples are illustrative. Measure quality and total costs on your own tasks, and check current endpoint terms before implementation.

## Related links

- [AI inference: from model output to production system](https://www.o10.io/ai-inference)
- [AI agents: useful actions, measurable outcomes](https://www.o10.io/ai-agents)
- [LLM routing: choose a model per task with explicit constraints](https://www.o10.io/routing)
- [Inference spend: calculate cost per accepted outcome](https://www.o10.io/inference-spend)

## Source URL

https://www.o10.io/ai