Workspaces Observability
Monitor, analyze, and optimize AI workspace performance across your organization
The Workspaces Observability module in AI Governance provides deep visibility into the activities, performance, and health of all workspaces across your organization. This powerful monitoring capability enables administrators to track interactions, identify bottlenecks, troubleshoot issues, and optimize performance to ensure AI solutions deliver maximum value.
Key Features
Activity Monitoring
Track all interactions and events within workspaces
Performance Analytics
Measure execution times and resource efficiency
Automation Tracking
Monitor trigger events and automated processes
Error Detection
Identify and analyze failures and exceptions
API Monitoring
Track API calls and integration activities
Visual Flow Analysis
Visualize interaction flows and process paths
Who Uses Workspaces Observability?
The Workspaces Observability module serves different roles within the organization.
IT operations teams use Workspaces Observability to:
- Monitor overall system health and performance
- Troubleshoot issues and bottlenecks
- Track resource utilization and efficiency
- Identify optimization opportunities
IT operations teams use Workspaces Observability to:
- Monitor overall system health and performance
- Troubleshoot issues and bottlenecks
- Track resource utilization and efficiency
- Identify optimization opportunities
AI engineers use Workspaces Observability to:
- Analyze interaction patterns and performance
- Debug automated workflows and processes
- Optimize AI agent behavior and responses
- Monitor integration points and data flows
Security teams use Workspaces Observability to:
- Monitor for unusual activity patterns
- Investigate potential security incidents
- Track access and authentication events
- Validate security control effectiveness
Core Capabilities
Workspace Overview Monitoring
Get comprehensive visibility into workspace activities and health:
- Interaction Metrics: Track total interactions and activity volumes
- Event Tracking: Monitor events generated within workspaces
- API Interaction Analysis: Measure API calls and integration activity
- Trigger Monitoring: Track automation triggers and executions
- Time Series Analysis: View activity patterns over time
Graph Analysis
Visualize and analyze process flows and interaction paths:
- Visual Flow Mapping: See how components interact within workspaces
- Path Analysis: Identify common and exceptional process paths
- Bottleneck Detection: Locate performance constraints in workflows
- Dependency Mapping: Visualize relationships between components
- Time Measurement: Track execution times along process paths
- Interactive Exploration: Drill down into specific paths and nodes
Automation Monitoring
Track automated processes and their performance:
- Trigger Analysis: Monitor events that initiate automated processes
- Execution Tracking: Track process completion and success rates
- Performance Metrics: Measure execution times and resource usage
- Error Detection: Identify failures and exception conditions
- Trend Analysis: Monitor automation performance over time
- Correlation Analysis: Connect automation activities to outcomes
Activity Detail Inspection
Examine specific activities and events in detail:
- Event Details: View complete information about specific events
- User Context: See which users initiated activities
- Timestamp Analysis: Track precise timing of activities
- Parameter Inspection: Examine inputs and outputs of processes
- Error Context: Get detailed information about failures
Best Practices for Workspace Observability
Baseline Establishment
Create performance baselines for comparing future metrics
Proactive Monitoring
Set up alerts for unusual patterns or performance issues
Regular Reviews
Schedule periodic reviews of observability data
Cross-Metric Analysis
Correlate multiple metrics to gain deeper insights
Historical Trending
Track performance changes over time to identify patterns
Documentation
Document findings and optimizations for knowledge sharing
Getting Started with Workspaces Observability
Access Workspaces Watcher
Navigate to the Workspaces Watcher section in the AI Governance dashboard.
Select a workspace
Choose a workspace from the list to start monitoring its activities.
You can search or filter to find specific workspaces of interest.
Review overview metrics
Examine the overview dashboard to get a high-level understanding of workspace activity. Pay attention to key metrics like triggers, interactions, and execution times.
Analyze graph data
Explore the visual graph to understand process flows and component interactions.
Click on nodes and edges to get more detailed information about specific elements.
Common Observability Scenarios
When addressing performance issues:
- Review execution time metrics to identify slow components
- Examine the process graph to locate bottlenecks
- Check resource utilization during peak activity periods
- Analyze error patterns and exception conditions
- Compare current performance against historical baselines
This methodical approach helps pinpoint and resolve performance problems.
When addressing performance issues:
- Review execution time metrics to identify slow components
- Examine the process graph to locate bottlenecks
- Check resource utilization during peak activity periods
- Analyze error patterns and exception conditions
- Compare current performance against historical baselines
This methodical approach helps pinpoint and resolve performance problems.
To understand how workspaces are being used:
- Analyze interaction volumes across time periods
- Identify peak usage times and potential patterns
- Examine the most commonly triggered automations
- Compare API vs. event-driven interactions
- Track user activity patterns and session characteristics
These insights help optimize workspaces for actual usage patterns.
When investigating errors or failures:
- Locate error events in the activities log
- Examine the context and parameters of failed operations
- Analyze the process path leading to the error
- Check for correlated errors or warning conditions
- Review similar historical incidents for patterns
This systematic approach helps identify root causes of issues.
For effective capacity planning:
- Review workspace utilization trends over time
- Identify growth patterns in interactions and API calls
- Analyze execution time trends as volume increases
- Project future resource needs based on growth patterns
- Test scalability through controlled load simulations
These activities ensure your infrastructure stays ahead of demand.
Key Metrics to Monitor
Triggers
Number of events that initiate automated processes
Event Interactions
Count of interactions driven by events within the system
API Interactions
Volume of calls to APIs and external services
Execution Time
Average and maximum time to complete operations
Error Rate
Percentage of operations that result in errors
Resource Utilization
CPU, memory, and network resources consumed
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