Performance decay over time/ Model drift is a common phenomenon for many agentic use cases.
Tips for Post-Deployment Model Monitoring and Retraining
Here is a step-by-step way to think about monitoring and logging data that can be leveraged to continuously improve and refine underlying models powering the agentic AI applications:
- Instrument logging at each interaction: record user queries, agent responses, user feedback, runtime stats, reasoning steps.
- Track model metrics (accuracy, latency, error rates) and business KPIs (conversion rate, task success).
- Define alert thresholds to trigger retraining or update workflows when metrics degrade significantly.
- Automate evaluation pipelines: periodic validation on new datasets promotes pipeline retraining.
- Use model distillation pipeline to swap in smaller models when cost vs. performance trade‑offs are favorable
Watch a recap of our technical session on how the latest NVIDIA AI blueprint for building data flywheels makes it easier to build this retraining and monitoring pipeline.