Route according to a requirement
Frontier model processing should solve an observable task need rather than serve as an automatic destination for every uncertain output. Separate the difficulty of understanding an input from the consequence of acting on the answer. Some decisions still belong with an authorized human reviewer.
Define the task and its required evidence. Locating text, normalizing a field, and interpreting an agreement are different activities. Keep the output contract clear enough that reviewers can distinguish a missing answer from an unsupported one.
Establish quality before choosing a route
Compare candidate routes against the same representative evaluation set and acceptance rubric. Include incomplete, unusual, and difficult cases. A broad model benchmark does not establish performance on your specific process.
Use inspectable routing signals such as failed validation, missing required evidence, or a document class with demonstrated baseline limitations. Do not assume a model's self-reported confidence is a calibrated probability. Evaluate any threshold against labeled examples before relying on it.
Enforce the data boundary first
A restricted document must not leave its approved environment merely because a local route failed. Keep permission and destination rules separate from quality tuning. Check the complete payload, including context, attachments, and identifying metadata.
Normalize accepted outputs into a consistent application schema and retain route metadata. Record the model configuration, prompt version, reason for escalation, and validation outcome. A schema check establishes structure, not factual correctness.
Define fallback and cost limits
Fallback should preserve the acceptance standard. A temporary service failure, an unsupported answer, and missing source information call for different responses. Put a ceiling on attempts and keep an explicit review path when another model call cannot resolve the issue.
Use the frontier routing playbook to plan a controlled rollout. The AI credits guide connects routing choices to total cost per accepted result, while the local LLM guide helps define an internal alternative when the data policy requires one.