How To Design AI Systems That Maintain Predictable Behavior Even When Models Are Updated, Replaced Or Improved Over Time
· Avery NXR
AI systems are built on moving foundations.
Models improve. APIs change. Capabilities evolve.
But while models are expected to change, systems are expected to remain stable.
This creates a fundamental tension:
How do you allow evolution without breaking behavior?
The Problem With Direct Model Dependence
Many systems are tightly coupled to specific models.
They assume:
Output formats Response styles Capabilities
When the model changes, these assumptions break.
Real World Impact
A model update may:
Change output structure Alter tone or reasoning Introduce variability
This can break downstream workflows silently.
Why This Is Dangerous
Because the system still “works.”
But behaves differently.
This creates hidden instability.
The Need For Abstraction Layers
Systems should not depend directly on models.
They should depend on interfaces.
What Abstraction Means
Instead of:
“Use model X for this task”
Define:
“This step produces structured output Y”
The model becomes interchangeable.
Design Principles For Stability
- Define Output Contracts
Specify what outputs should look like.
- Validate Model Outputs
Ensure outputs meet expectations before use.
- Version Models Explicitly
Track which model version is being used.
- Test Before Deployment
Evaluate new models against existing workflows.
- Gradual Rollouts
Introduce changes incrementally.
How Avery NXR Handles Model Changes
Avery NXR separates:
Model logic Workflow logic
This allows models to evolve without breaking workflows.
The Deeper Insight
AI systems should treat models as replaceable components.
Not foundational dependencies.
Final Thought
Systems should evolve with models.
Not break because of them.