AI Agent A/B Testing Pipeline with Performance Tracking

Set up custom events to track AI agent interactions: 'agent_query_received', 'agent_response_generated', 'user_satisfaction_rating'. Include properties like agent_version, response_time, and accuracy_score.

Create a feature flag called 'ai_agent_version' with two treatments: 'control' (current agent) and 'treatment' (new agent). Set traffic allocation to 50/50 and target specific user segments.

Create dashboards tracking response time, error rates, and user satisfaction scores for both agent versions. Set up anomaly detection to catch performance degradation automatically.

Create a Zap that triggers when Datadog detects performance issues (response time >3s or error rate >5%). Automatically post detailed alerts to your team's Slack channel.

Set up automated notebooks that pull data from Mixpanel and Datadog APIs to create comprehensive A/B test reports with statistical significance calculations and recommendations.