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.

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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

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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

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Core Capabilities

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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

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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

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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

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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

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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

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Getting Started with Workspaces Observability

1

Access Workspaces Watcher

Navigate to the Workspaces Watcher section in the AI Governance dashboard.
2

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.
3

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.
4

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.

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Common Observability Scenarios

When addressing performance issues:
  1. Review execution time metrics to identify slow components
  2. Examine the process graph to locate bottlenecks
  3. Check resource utilization during peak activity periods
  4. Analyze error patterns and exception conditions
  5. Compare current performance against historical baselines
This methodical approach helps pinpoint and resolve performance problems.

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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