Monitoring & Analytics
Track, measure, and optimize your AI agents' performance with comprehensive monitoring and analytics tools.
Overview
Understanding how your agents perform is critical to delivering value. Our monitoring and analytics suite provides visibility into:
Usage patterns - Who's using agents and how often
Response quality - How accurate and helpful responses are
Performance metrics - Response times and system health
Cost analysis - Token usage and associated costs
User satisfaction - Feedback and ratings
Key Tools
Your central hub for monitoring agent performance and usage. Get real-time insights with interactive visualizations.
What You'll See:
Total queries and trends over time
Most active agents and users
Popular questions and topics
Geographic usage distribution
Success rates and error tracking
Use Cases:
Track adoption across your organization
Identify high-value use cases
Spot usage anomalies
Demonstrate ROI to stakeholders
Review conversations and improve agent responses through active learning and human feedback.
Key Features:
Conversation review queue
Thumbs up/down feedback collection
Annotation and correction tools
Training data curation
Quality assurance workflows
Use Cases:
Improve response accuracy
Identify knowledge gaps
Curate training examples
Quality control for customer-facing agents
Systematically measure and improve agent performance with automated evaluations.
Capabilities:
Automated testing of agent responses
Benchmark datasets for comparison
A/B testing different configurations
Regression detection
Custom evaluation metrics
Use Cases:
Test changes before deployment
Track improvements over time
Compare different prompts or models
Ensure consistent quality
Optimize response speed, accuracy, and cost through systematic tuning of agent parameters.
What You Can Tune:
RAG strategy selection
Chunking parameters
Retrieval settings
Model selection and parameters
Caching strategies
Use Cases:
Reduce latency for time-sensitive applications
Improve accuracy for critical use cases
Balance quality vs. speed trade-offs
Monitor and reduce costs associated with AI operations while maintaining quality.
Cost Visibility:
Token usage by agent, user, and time period
Model costs (embeddings, completions, reranking)
Data processing costs
Total cost of ownership
Optimization Strategies:
Caching frequently requested information
Choosing cost-effective models
Optimizing context window usage
Reducing unnecessary API calls
Monitoring Best Practices
1. Set Baseline Metrics
Before optimization, establish baseline performance:
Current response times
Typical accuracy rates
Normal usage patterns
Baseline costs
2. Define Success Metrics
Determine what success looks like for your use case:
Target response accuracy (e.g., 90%+ thumbs up)
Acceptable latency (e.g., <3 seconds)
Cost per query targets
Adoption rates
3. Monitor Continuously
Set up regular monitoring routines:
Daily: Check for errors or anomalies
Weekly: Review usage trends and costs
Monthly: Analyze conversation quality
Quarterly: Evaluate ROI and strategic impact
4. Act on Insights
Use data to drive improvements:
Add missing knowledge to fill gaps
Adjust prompts based on feedback
Optimize performance bottlenecks
Scale resources based on usage
5. Close the Loop
Create feedback cycles:
User feedback → Training data
Analytics insights → Configuration changes
Performance issues → Infrastructure upgrades
Cost trends → Optimization initiatives
Key Metrics to Track
Usage Metrics
Total Queries: Overall volume of requests
Active Users: Unique users engaging with agents
Queries per User: Average engagement level
Peak Usage Times: When demand is highest
Quality Metrics
User Satisfaction: Thumbs up/down ratios
Response Accuracy: Correct vs. incorrect answers
Source Attribution: Percentage with citations
Fallback Rate: How often "I don't know" is returned
Performance Metrics
Response Time: End-to-end latency
Time to First Token: Perceived responsiveness
Retrieval Time: Knowledge base query speed
Error Rate: Failed requests
Cost Metrics
Cost per Query: Average spend per request
Token Usage: Input and output tokens
Model Costs: By model type (embeddings, completions)
Cost by Agent: Which agents are most expensive
Dashboards & Reports
Real-Time Dashboard
Monitor current activity:
Active conversations
Recent queries
System health indicators
Error alerts
Executive Summary
High-level overview for stakeholders:
Adoption trends
ROI metrics
Cost savings
Strategic insights
Operational Reports
Detailed reports for optimization:
Agent-by-agent performance
User engagement patterns
Knowledge base coverage
Technical performance metrics
Custom Reports
Build your own reports using:
Data exports
Webhook integrations
Third-party analytics tools
Alerting & Notifications
Set up proactive alerts for:
Error Spikes: Sudden increase in failures
Performance Degradation: Response times increase
Cost Overruns: Budget thresholds exceeded
Quality Issues: User satisfaction drops
Usage Anomalies: Unusual activity patterns
Configure notifications via:
Email
Slack (Slack App)
Webhooks (Webhooks Guide)
PagerDuty or other incident management tools
Optimization Workflow
Identify: Use analytics to find improvement opportunities
Hypothesize: Form theories about what might help
Test: Use evaluation framework to validate changes
Deploy: Roll out improvements to production
Measure: Track impact with monitoring tools
Iterate: Continue the cycle
Common Monitoring Scenarios
Scenario 1: Agent Not Performing Well
Symptoms: Low satisfaction scores, high fallback rate
Investigation Steps:
Check Analytics Dashboard for patterns
Review conversations in Inbox
Run Evaluations to quantify issues
Identify missing knowledge or prompt problems
Resolution: Update knowledge base or adjust prompts
Scenario 2: High Costs
Symptoms: Costs increasing faster than expected
Investigation Steps:
Check Cost Optimization dashboard
Identify high-cost agents or users
Analyze token usage patterns
Review model selection
Resolution: Implement caching, optimize context windows, or switch models
Scenario 3: Slow Response Times
Symptoms: Users complaining about latency
Investigation Steps:
Check Performance Tuning metrics
Identify bottlenecks (retrieval, model, network)
Review system load and resource usage
Resolution: Optimize retrieval, enable caching, or scale infrastructure
Integration with Other Tools
Export Data
Export analytics data to:
Business intelligence tools (Tableau, Power BI)
Data warehouses (Snowflake, BigQuery)
Spreadsheets for ad-hoc analysis
API Access
Access metrics programmatically:
Developer API endpoints
Custom dashboard integration
Automated reporting workflows
Webhooks
Receive real-time events:
Stream data to analytics platforms
Trigger automated workflows
Advanced Topics
Statistical Analysis
Trend analysis and forecasting
Cohort analysis for user behavior
A/B test statistical significance
Outlier detection
Custom Metrics
Define domain-specific KPIs
Create composite scores
Build custom evaluation criteria
Machine Learning on Metrics
Anomaly detection with ML models
Predictive scaling
Automated optimization recommendations
Next Steps
Start Monitoring: Log in to your Analytics Dashboard
Set Up Inbox: Configure your Inbox & Training workflow
Define Metrics: Decide what success looks like with Evaluation Framework
Optimize: Improve performance with Performance Tuning
Manage Costs: Control spending with Cost Optimization
For more detailed guidance, explore the individual topics listed above.
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