Observability

Monitor workspace performance, errors, costs, and request traces

Observability provides deep visibility into your AI workspaces, tracking performance metrics, error rates, LLM costs, and detailed request traces. Access it from Observability in the Governe sidebar.

Dashboard Overview

The observability dashboard shows key metrics at a glance:

Metric	Description
Health Score	0-100 composite score based on errors, latency, and availability
Total Executions	Number of automation runs in the selected period
Error Rate	Percentage of executions that failed
P95 Latency	95th percentile response time
LLM Costs	Total cost of LLM calls

Health Score

The health score combines multiple factors into a single 0-100 metric:

Status	Score Range	Color
Healthy	90-100	Green
Degraded	70-89	Yellow
Critical	0-69	Red

Factor Breakdown

Factor	Weight	Calculation
Error Rate	40%	Lower error rate = higher score
Latency	30%	Lower latency = higher score
Availability	30%	Higher uptime = higher score

Performance Metrics

Latency Percentiles

Track response time distribution:

Percentile	Description
P50	Median response time
P90	90% of requests faster than this
P95	95% of requests faster than this
P99	99% of requests faster than this

Slowest Automations

Identify bottlenecks by viewing automations ranked by average execution time.

Timeline View

Visualize performance over time with:

• P95 latency trend (area chart)
• Request volume (line overlay)

This helps correlate load spikes with latency increases.

Error Tracking

Error Distribution

View errors grouped by:

• Type: InvalidExpressionSyntax, FetchError, TimeoutError, etc.
• Automation: Which automations generate the most errors

Error Rate Trend

Track error rate changes over time to identify:

• New bugs introduced
• External service outages
• Configuration issues

Recent Errors

View the latest errors with:

• Timestamp
• Error type and message
• Automation name
• Correlation ID (for tracing)

LLM Costs

Monitor AI model consumption and costs:

Cost Breakdown

Metric	Description
Total Calls	Number of LLM API calls
Total Cost	Estimated cost in USD
Total Tokens	Input + output tokens consumed
Carbon Impact	Estimated CO2 emissions (kg)

By Model

See which models consume the most resources:

• Model name
• Number of calls
• Cost percentage
• Token count

By Automation

Identify which automations drive LLM costs:

• Automation name
• Call count
• Relative usage

Cost Timeline

Track daily cost trends to:

• Forecast monthly spend
• Identify cost anomalies
• Plan capacity

Request Traces

Trace individual requests through the entire execution flow.

Accessing Traces

1. From the Observability dashboard, click View Traces
2. Or click a correlation ID in the error list

Trace View

Each trace shows:

Element	Description
Summary	Event count, duration, user info
Timeline	Waterfall view of all events
Automations	List of automations executed
Errors	Any errors encountered

Timeline Visualization

The waterfall chart shows:

• Each automation as a horizontal bar
• Start time offset from request start
• Duration of each step
• Errors highlighted in red

Dependency Graph

Visualize how automations call each other.

Graph View

• Nodes: Automations and installed apps
• Edges: Calls between components
• Edge weight: Call frequency

Use Cases

• Understand system architecture
• Identify critical paths
• Find orphaned automations

Anomaly Detection

Automatic detection of unusual patterns.

Anomaly Types

Type	Description
Error Spike	Sudden increase in error rate
Latency Spike	Sudden increase in response time
Traffic Spike	Unusual increase in request volume
Traffic Drop	Unusual decrease in activity

Severity Levels

Severity	Threshold
Warning	2x baseline
Critical	3x baseline

Configuration

Adjust detection sensitivity:

• Low: Only alert on significant anomalies
• Medium: Balanced detection (default)
• High: Alert on minor deviations

Period Comparison

Compare metrics between two time periods.

How to Compare

1. Select current period dates
2. Select comparison period dates
3. View side-by-side metrics

Compared Metrics

• Execution count
• Error count and rate
• Average and P95 latency
• Unique users

Trend Indicators

• Up: Increase > 5%
• Down: Decrease > 5%
• Stable: Within ±5%

Real-Time Feed

Monitor events as they happen.

Event Types

Type	Description
runtime.automations.executed	Automation completed
runtime.fetch.completed	HTTP fetch succeeded
runtime.fetch.failed	HTTP fetch failed
runtime.llm.completed	LLM call completed
error	General error event

Filtering

• All: Show all events
• Errors: Only error events
• Executions: Only completion events

Time Window

Configure how far back to show events (default: 60 minutes).

Time Range Selection

All views support time range selection:

Period	Description
1h	Last hour
24h	Last 24 hours
7d	Last 7 days
30d	Last 30 days
Custom	Specific date range

Platform Summary (Admin Only)

Platform administrators see a platform-wide summary:

• Aggregate health score across all workspaces
• Total platform requests
• Combined LLM costs
• Peak capacity usage

Best Practices

Set Baselines

Establish normal performance baselines for comparison

Monitor Anomalies

Enable anomaly detection for early warning

Track Costs

Review LLM costs weekly to prevent surprises

Use Traces

Investigate errors using correlation IDs

Troubleshooting

High Latency

1. Check Slowest Automations for bottlenecks
2. Review the Timeline for load correlation
3. Examine traces for slow steps
4. Check external API response times

High Error Rate

1. View Error Distribution by type
2. Check Recent Errors for patterns
3. Trace errors using correlation IDs
4. Review dependency health

Cost Spikes

1. Check By Model breakdown
2. Identify expensive automations
3. Review Cost Timeline for anomalies
4. Check for loops or inefficient prompts

Next Steps

Model Governance

Control model access and costs

Audit Logs

Track administrative changes