Practical guide | October 25, 2026
A new model can improve one task while changing the behavior your workflow depends on. Before switching an existing automation, test the whole job rather than relying on a benchmark headline.
Save the current baseline
Record the model configuration, prompt, tool permissions and representative outputs. Keep examples of past failures as well as ordinary tasks. Without a baseline, it is difficult to tell whether a new problem came from the model or another part of the workflow.
Check the boundaries
Test ambiguous instructions, unavailable sources and requests outside the intended scope. Verify that the system preserves uncertainty and stops for required approvals. If the agent can publish or send messages, use a test destination with harmless content during evaluation.
Validate integrations
Inspect structured fields, tool arguments and error handling. Check whether long inputs are truncated, whether citations support the text, and whether retries create duplicate actions. A model that writes better prose may still need integration changes.
Measure review effort
Compare factual accuracy, completion rate, latency, usage cost and human editing time on the same examples. Record the number of runs and the test conditions. Avoid presenting a handful of internal examples as a general performance ranking.
Roll out with a way back
Start with a limited workload and a named owner who can pause the automation. Keep the previous configuration available when practical. Define what would trigger rollback, such as an unauthorized action, repeated unsupported claims or unusable output.
OpenAI's evaluation guidance describes using explicit tasks and criteria to assess model outputs. This checklist applies that principle to a small team's change process; it does not establish that any particular update is safe or unsafe.
Primary reference: OpenAI: Working with evals. Review the provider's current release notes before changing a production configuration.