Skip to main content

​
Overview

A Feature Flag Manager powered by Pylar monitors feature flag performance, analyzes user impact, and provides data-driven recommendations for rollouts and rollbacks.

​
What the Agent Needs to Accomplish

The agent must:
  • Monitor feature flag status
  • Analyze flag performance metrics
  • Track error rates and user impact
  • Recommend rollback decisions
  • Optimize rollout percentages
  • Track feature flag effectiveness

​
How Pylar Helps

Pylar enables the agent by:
  • Unified Flag View: Combining flag data, error rates, and user metrics
  • Real-time Monitoring: Querying current flag performance
  • Impact Analysis: Automated impact calculations
  • Decision Support: Data-driven rollout recommendations

​
Without Pylar vs With Pylar

​
Without Pylar

Challenges:
  • ❌ Manual flag monitoring
  • ❌ Complex error correlation
  • ❌ Time-consuming impact analysis
  • ❌ Limited real-time visibility
Implementation Complexity: ~3-4 weeks

​
With Pylar

Benefits:
  • ✅ Automated flag monitoring
  • ✅ Real-time performance tracking
  • ✅ Instant impact analysis
  • ✅ Clear rollback recommendations
Implementation Complexity: ~4-5 hours

​
Step-by-Step Implementation

​
Step 1: Connect Data Sources

  1. Connect Feature Flag Platform (LaunchDarkly, Split.io)
  2. Connect Error Tracking (Sentry, Bugsnag)
  3. Connect Product Analytics (User behavior, engagement)

​
Step 2: Create Flag Views

Feature Flag Performance View: 
CREATE VIEW flag_performance AS
SELECT 
  f.flag_name,
  f.enabled_percentage,
  f.status,
  COUNT(DISTINCT u.user_id) as users_affected,
  e.error_count,
  e.error_rate,
  a.engagement_score,
  -- Risk score
  (e.error_rate * 50 + 
   CASE WHEN a.engagement_score < 50 THEN 30 ELSE 0 END +
   CASE WHEN f.enabled_percentage > 50 AND e.error_rate > 0.05 THEN 20 ELSE 0 END) as risk_score
FROM feature_flags.flags f
LEFT JOIN errors.error_summary e ON f.flag_name = e.flag_name
LEFT JOIN analytics.user_engagement a ON f.flag_name = a.flag_name
GROUP BY f.flag_name, f.enabled_percentage, f.status, e.error_count, e.error_rate, a.engagement_score;

​
Step 3: Create MCP Tools

Tool 1: Monitor Feature Flag
  • monitor_feature_flag(flag_name: string)
Tool 2: Analyze Flag Performance
  • analyze_flag_performance(flag_name: string, hours_back: number)
Tool 3: Recommend Rollback
  • recommend_rollback(flag_name: string, threshold: number)
Tool 4: Optimize Rollout
  • optimize_rollout(flag_name: string, target_percentage: number)

​
Example Agent Interactions

User: “Should we rollback the new checkout feature?” Agent: “Feature Flag Analysis: ‘new_checkout’
  • Status: 35% enabled
  • Error Rate: 2.1% (Threshold: 1%)
  • User Impact: 1,200 users affected
  • Engagement: -15% vs baseline
  • Risk Score: 68 (High)
Recommendation: ROLLBACK - Error rate exceeds threshold and engagement is declining”

​
Outcomes

  • Incident Detection: 60% faster detection
  • Rollback Decisions: 50% more accurate decisions
  • User Impact: 40% reduction in negative impact
  • Feature Success: 25% improvement in successful rollouts

​
Next Steps