Low-latency model inference

Groq Cloud runs language models on its own hardware built to return a response quickly, which matters for a workflow where an agent is waiting on a model's output before it can act. A Beam agent sends a prompt to Groq Cloud as part of a larger task, such as summarizing a document or classifying a message, and reads back the model's response as soon as it returns rather than a slow round trip holding up the rest of the workflow. Where the response comes back incomplete, or the request times out, the agent retries within the account's approved limits and, failing that, passes the task to a person rather than acting on a partial result.

Model selection per task

Groq Cloud can host more than one model, so a task calling for a quick classification and a task calling for a longer written response may not need the same model underneath. Where the account owner has set which model handles which kind of request, a Beam agent routes each prompt to the model assigned to it rather than defaulting to a single one for every task. This keeps a straightforward request from being sent to a heavier model than it needs, and keeps a more demanding request from being sent to one that is not suited to it. Any request that does not clearly match a configured model type is left for a person to route manually.

Confidence-based output review

A model response coming back from Groq Cloud can carry a confidence signal or fall outside an expected format, and a Beam agent checks for that before treating the response as final. Where the output matches what the account owner defined as an acceptable answer, the agent applies it, such as filing a summary or updating a classification field. Where the output is malformed, unusually short, or otherwise does not fit the expected shape, the agent does not attempt to interpret it further and instead passes the original prompt and the model's response to a person, rather than guessing at what the model meant.