Agent Drift

Agent Drift refers to the gradual degradation of AI agent performance over time, where the quality, accuracy, or behavior of AI responses deviates from expected standards without explicit model changes.

1. Data Distribution Shifts: The real-world data the AI encounters may change over time, causing the model to perform differently than during training.

2. Model Decay: Without retraining, AI models may gradually lose effectiveness as they encounter new patterns or edge cases.

3. Prompt Engineering Changes: Subtle changes in how prompts are constructed or how context is provided can lead to different response patterns.

4. Knowledge Base Updates: Changes to underlying knowledge bases or business rules may create inconsistencies in AI responses.

5. Context Window Limitations: As conversations become longer or more complex, AI agents may lose track of earlier context, leading to inconsistent responses.

Detecting Agent Drift: AI observability platforms like Oversai monitor key performance indicators over time, identifying when metrics such as: - Hallucination rates increase - Response accuracy decreases - Customer satisfaction scores decline - Compliance violations become more frequent

Preventing and Mitigating Drift: 1. Continuous Monitoring: Real-time tracking of performance metrics to catch drift early.

2. Automated Alerts: Setting thresholds that trigger alerts when performance degrades.

3. Regular Model Evaluation: Periodic assessment of AI agent performance against updated test sets.

4. Retraining and Fine-tuning: Updating models with new data or adjusting parameters to restore performance.

5. A/B Testing: Comparing new model versions against current production models before deployment.

Oversai's platform includes advanced drift detection capabilities, alerting you when AI agent performance begins to degrade so you can take corrective action before it impacts customer experience.