Prevent model drift by verifying changes after model or prompt updates
Every LLM upgrade or prompt change may break guardrails or produce new failure modes. Recurring tests detect regressions immediately.
What's at stake
- Model updates can change behavior in subtle, unexpected ways
- Prompt modifications may introduce regressions in previously working scenarios
- Guardrails calibrated for one model version may not work for another
- Drift is often invisible until it causes a customer-facing failure
- Enterprise customers require proof that updates don't degrade safety
How to solve this
Every change to your AI system—model upgrades, prompt modifications, context changes—can alter behavior. A new model version might be better at some tasks but worse at following safety instructions. A prompt tweak that improves one scenario might break another.
Model drift is insidious because it's gradual and hard to detect. Your agent might slowly become less accurate, less safe, or less aligned with policy without any obvious trigger.
The solution is continuous testing against a fixed baseline. Every change is validated against known-good scenarios. Regressions are detected immediately, before they reach production.
How Superagent prevents this
Superagent provides guardrails for AI agents—small language models purpose-trained to detect and prevent failures in real time. These models sit at the boundary of your agent and inspect inputs, outputs, and tool calls before they execute.
Superagent's Adversarial Tests establish behavioral baselines and detect drift:
- Baseline establishment: Initial tests capture expected behavior for critical scenarios
- Change detection: Every update triggers a test run against the baseline
- Regression identification: Behavioral changes are flagged with specific evidence
- Continuous monitoring: Recurring tests catch gradual drift over time
When you update your model or modify prompts, tests run automatically. Results show exactly what changed—which scenarios now fail, which behaviors shifted, which guardrails no longer hold.
Your team can review changes before deployment. If regressions are acceptable, update the baseline. If not, roll back or fix the change. Either way, you know exactly what changed and why.