In 30 seconds
Your workflow depends on things you do not control: the AI model, connected services, their behavior and their rules. When they change, your workflow can change too, silently. Know what you depend on, and watch those dependencies.
Why this comes up
An AI workflow rarely stands alone. It relies on a model from a vendor, perhaps a retrieval service, and integrations to other systems. Those providers update their products, adjust behavior, change limits, and occasionally retire features. A change on their side can shift your results without you touching anything, a common cause of drift.
Know your dependencies
You cannot monitor what you have not listed. Write down what your workflow depends on externally:
- The AI model and vendor.
- Any retrieval, storage or processing services.
- Integrations to other systems, internal or external.
For each, note what would happen to your workflow if it changed or became unavailable. This turns invisible dependencies into a list you can actually watch.
Watch for changes
- Follow the vendor's change announcements for the model and services you use.
- Rerun your golden test set on a schedule, so a behavior change shows up as a drop in results even if you missed the announcement.
- Be cautious with automatic updates to a model version. A new version can behave differently on your specific cases. Where you can, test a change before it reaches production.
Plan for a dependency changing
For your important dependencies, have a rough plan: what you would do if the vendor changed the model's behavior, raised a limit, or retired a feature. You do not need a detailed contingency for everything, but the critical dependencies deserve a thought before the change arrives, not after.
A small example
A team's extraction quality dropped one week with no change on their side. A scheduled test-set rerun caught it. The cause was a model version update the vendor had rolled out. Because they had a plan, they pinned to the tested version, re-evaluated the new one deliberately, and moved over once it passed.
What this does not solve
Monitoring dependencies helps you react to external changes; it does not prevent them, and you cannot anticipate every one. The reliable safeguard is your own evaluation: a test set you rerun regularly will reveal a harmful change even when you did not see it coming.
Good enough to move on when
- Your external dependencies are listed.
- You follow change announcements for the important ones.
- Your test set is rerun on a schedule to catch behavior changes.
- Critical dependencies have a rough plan for changing.
