Skip to main content
Monitor performance and gain insights into your app’s usage.

​
Overview

The analytics dashboard provides real-time visibility into how your agentic app performs—tracking users, sessions, messages, and resource consumption across agents, tools, and models.

​
Dashboard Components

​
Key Metrics

Usage Overview
UsersSessionsMessagesTokens
1,234
(↑ 12%)		3,456
(↑ 8%)		45,678
(↑ 15%)		2.3M
(↑ 18%)
MetricDescription
UsersUnique active users in the period
SessionsTotal conversation sessions
MessagesMessages exchanged (user + agent)
TokensTotal token consumption
Compare current period to previous: Daily/hourly breakdowns, Week-over-week comparisons, or Growth trends.

​
Run Analytics

Track execution across your app’s components.

​
Agent Runs Example

AgentRunsAvg ResponseTokensSuccess
Support Agent1,2342.3s450K98.5%
Billing Agent5671.8s180K99.1%
Order Agent8903.1s320K97.8%

​
Tool Runs Example

Tool TypeToolRunsAvg TimeSuccess
Workflowget_order890450ms99.2%
Codevalidate_input1,200120ms99.8%
MCPcrm_lookup456890ms96.5%
Knowledgefaq_search2,100340ms99.9%

​
Model Runs Example

ModelInvocationsAvg LatencyTokensCost
gpt-4o3,4001.2s1.8M$45.20
gpt-3.51,2000.4s320K$0.64

​
Traces

Traces provide detailed visibility into individual request lifecycles.

​
What’s in a Trace

Trace: req_abc123
├── Start: 2024-01-15 14:30:22.123
├── End: 2024-01-15 14:30:25.456
├── Duration: 3.333s
│
├── Events
│   ├── [14:30:22.123] Request received
│   ├── [14:30:22.145] Agent selected: Support Agent
│   ├── [14:30:22.200] Tool invoked: get_order_status
│   ├── [14:30:22.650] Tool response received
│   ├── [14:30:22.700] LLM generation started
│   ├── [14:30:25.400] LLM generation completed
│   └── [14:30:25.456] Response sent
│
├── Spans
│   ├── Agent Processing: 3.2s
│   ├── Tool Execution: 450ms
│   └── LLM Generation: 2.7s
│
└── Generations
    └── Support Agent response
        ├── Model: gpt-4o
        ├── Input tokens: 1,234
        ├── Output tokens: 256
        └── Latency: 2.7s

​
Trace Benefits

  • Debug request flow issues.
  • Identify bottlenecks.
  • Understand agent behavior.
  • Optimize performance.

​
Sessions

Sessions track continuous user interactions.

​
Session View

Session: sess_xyz789
├── User: user_456
├── Started: 2024-01-15 14:25:00
├── Duration: 12 minutes
├── Traces: 5
│
├── Trace 1: "What's my order status?"
│   └── Agent: Support Agent, Duration: 3.3s
│
├── Trace 2: "When will it arrive?"
│   └── Agent: Support Agent, Duration: 2.1s
│
├── Trace 3: "Can I change the address?"
│   └── Agent: Order Agent, Duration: 4.5s
│
├── Trace 4: "What's the cost?"
│   └── Agent: Billing Agent, Duration: 1.8s
│
└── Trace 5: "Thanks, that's all"
    └── Agent: Support Agent, Duration: 0.8s

Total Cost: $0.12

​
Generations

Track individual LLM outputs within traces.

​
Generation Details

FieldValue
Modelgpt-4o
Input tokens1,234
Output tokens256
Latency2.7s
Cost$0.032
Temperature0.7

​
Quality Assessment

  • Review response quality.
  • Identify hallucinations.
  • Track instruction following.

​
Filtering

Customize your analytics view:
FilterOptions
Time RangeLast hour, Last 24 hours, Last 7 days, Last 30 days, or Custom range
EnvironmentDraft (development), Staging, or Production
DimensionsBy agent, tool, model, or user segment

​
Exporting Data

Download analytics for external analysis:

​
Available Exports

  • CSV: Spreadsheet-compatible
  • JSON: Programmatic analysis
  • PDF: Shareable reports

​
Export Options

export:
  format: csv
  date_range: last_30_days
  include:
    - sessions
    - traces
    - generations
    - tool_runs
  filters:
    environment: production
    agent: Support Agent

​
Alerts

Configure notifications for important events:

​
Alert Types

alerts:
  - name: High error rate
    condition: error_rate > 5%
    window: 1 hour
    action: email

  - name: Slow responses
    condition: avg_latency > 5s
    window: 15 minutes
    action: slack

  - name: Cost spike
    condition: daily_cost > $100
    window: 1 day
    action: email

​
Audit Logs

Track all changes made across your account.

​
What’s Logged

  • User actions (create, update, delete)
  • Configuration changes
  • Deployments
  • Access events

​
Log Entry

Event: Tool Updated
User: alice@company.com
Time: 2024-01-15 14:30:00
Details:
  Tool: get_order_status
  Changes:
    - timeout: 30s → 60s
    - description: Updated

​
Compliance Uses

  • Track who changed what.
  • Maintain audit trail.
  • Support security reviews.

​
Best Practices

​
Monitor Key Metrics

Focus on metrics that matter:
  • Success rate: Are requests completing successfully?
  • Latency: Is performance acceptable?
  • Cost: Is spending within budget?
  • User satisfaction: Are users getting help?

​
Set Baselines

Establish normal ranges to detect anomalies: 
baselines:
  success_rate: 95-99%
  avg_latency: 1-3s
  daily_cost: $20-50

​
Review Regularly

  • Daily: Quick health check
  • Weekly: Trend analysis
  • Monthly: Deep dive and optimization

​
Act on Insights

Use analytics to drive improvements:
  • Slow agent? Optimize tools or prompts.
  • High error rate? Review configurations.
  • Cost spike? Check token usage patterns.