AI Operations
What happens after the model changes?
A model update can change behaviour without breaking an API. The prompt, retrieved context and tools can change it too. FutureOps helps teams evaluate those changes and retain control once an AI feature is in use.
The demonstration is a small sample.
A convincing output says little about the cases the demonstration did not include. Start with examples that matter to the people using the feature, including the cases where a plausible answer would be wrong.
A release needs more than a model version
Evaluate the whole workflow
Keep representative cases and explicit acceptance criteria. Record the prompt, context and tools alongside the model so a result can be investigated.
Release a change you can identify
Version the parts that affect behaviour. Decide what can be rolled back and what must be stopped if the change is unacceptable.
Learn from actual use
Unexpected outputs should inform the next evaluation. Observation is useful when it produces evidence the team can act on.
Where a person needs to decide
Before a consequential action
An agent calling a tool is doing more than producing text. Agree which actions need approval and what information the reviewer needs.
When the evidence is insufficient
Define when the workflow should pause or hand over. A confident answer does not remove uncertainty.
Put the controls into use
Choose a bounded workflow
Establish what the feature may do and collect examples that expose its limits.
Review the next change
Evaluate the proposed release against those examples. Keep the decision and the evidence together.
Revisit the cases
Use failures and interventions to improve evaluation. The checks need to change as the workflow does.