Run low-latency model inference

Cerebras runs large language model inference through an API built for speed, returning completions from a prompt with very low latency. An agent can submit a request when a workflow needs a model to generate or evaluate text, such as drafting a reply or classifying an incoming item, and read the response as soon as it comes back. This fits workflows where a person or a downstream system is waiting on the result in real time. Outputs that look uncertain, incomplete, or outside what the rule expects are routed to a person to check before they are acted on.

Monitor request status and usage

Every call to Cerebras produces a result the agent can log, including whether the request succeeded, how long it took, and what it consumed against the account's usage. An agent tracks this so a team can see how a workflow that depends on Cerebras is performing over time, without someone checking a dashboard manually. It records failed calls with the reason returned so the pattern is visible rather than silent. When failures repeat, latency rises past what the workflow expects, or usage approaches a limit the account has, the agent surfaces this to a person instead of continuing to retry unattended.

Route requests across available models

Where an account has access to more than one model on Cerebras, an agent can select which one to call based on a rule the team has approved for the task at hand, such as a faster model for routine classification and a larger one for a task that needs more reasoning. If the preferred model is unavailable or the account's access does not cover it, the agent falls back to the approved alternative, and if no suitable model is available, it flags the request to a person.