Analytics

Track agent performance, compare experiments, and identify optimization opportunities across your workspace

Overview

The analytics dashboard provides granular, real-time metrics for your conversational agents. You can break down performance across multiple dimensions — by agent, branch, time period, language, call type, model, and more — to understand exactly how your agents are performing in production.

Analytics data is powered by a high-performance columnar database, enabling fast queries across large volumes of conversation data with flexible filtering and grouping.

Accessing analytics

Navigate to the Analytics tab in your agents dashboard. You can view metrics across your entire workspace or filter down to a specific agent.

Analytics dashboard General tab showing call count, average duration, total cost, and call
volume over time

When running experiments, you can jump directly to branch-filtered analytics from the agent configuration page using the View Analytics button. This pre-applies the agent and branch filters so you can compare variant performance immediately.

Time range and granularity

Select the time range for your analysis using the date picker at the top of the dashboard. You can choose from preset ranges or define a custom window.

The dashboard automatically adjusts the granularity of time-series charts based on your selected range — hourly buckets for short ranges, daily or weekly for longer ranges.

Available metrics

Conversations

  • Call count — total number of conversations in the selected period
  • Total duration — aggregate conversation time
  • Average duration — mean conversation length
  • Total cost — total spend across all conversations
  • Average cost — mean cost per conversation

Performance

  • Agent response latency — time for the agent to respond (median and percentiles)
  • Error rate — percentage of conversations with errors
  • Error breakdown — errors categorized by type (tool failures, LLM errors, connection issues)

Success evaluation

If you have evaluation criteria configured, the dashboard shows success, failure, and unknown rates for each criterion. This is the primary way to measure business outcomes across experiments.

Data collection

If you have data collection configured, collected values are available as filterable dimensions in the dashboard.

Language breakdown

See the distribution of conversations across languages. This is useful for understanding multilingual adoption and comparing agent performance across different languages.

Active calls

The dashboard displays the current number of active calls in real time. This reflects ongoing sessions across your workspace and is also available via the API.

Filtering

Narrow your analytics view by applying filters on any combination of dimensions:

Tools tab showing average error rate and average tool latency grouped by tool
type

FilterDescription
AgentView metrics for a specific agent
BranchCompare performance across experiment branches
Call typeFilter by inbound, outbound, or web calls
LanguageFilter by conversation language
Conversation sourceFilter by how the conversation was initiated (widget, phone, API)
LLM modelCompare performance across different language models
TTS modelCompare performance across text-to-speech models
ASR modelCompare performance across speech recognition models
Tool typeFilter by specific tools used in conversations
Error typeIsolate conversations with specific error categories
Evaluation criteriaFilter by success evaluation results

Grouping

Group metrics by any of the filterable dimensions to break down aggregate numbers.

LLMs tab showing LLM time to first sentence over
time

Turn taking latency chart showing p50, p90, and p99
percentiles

For example:

  • Group by branch to compare experiment variants side by side
  • Group by language to see how agents perform across languages
  • Group by LLM model to compare model performance and cost
  • Group by call type to understand differences between inbound and outbound calls

Multiple grouping dimensions can be combined for deeper analysis.

Using analytics with experiments

Analytics is the primary tool for measuring experiment outcomes. The recommended workflow:

1

Filter by agent

Select the agent running your experiment.
2

Group by branch

Break down all metrics by branch to see variant-level performance.

3

Compare key metrics

Look at the metrics that matter for your hypothesis — success evaluation results, conversation duration, cost, error rates.

4

Decide and act

When one variant consistently outperforms, increase its traffic share or merge it to main.

You can jump directly to this view from the agent configuration page by clicking the View Analytics button next to your traffic deployment settings. This pre-applies the correct agent and branch filters.

Next steps