Dashboard Metrics Overview

Your AgentSight dashboard provides real-time insights into user engagement, behavior, and agent performance.
Some metrics are tracked automatically, while others require additional fields to be passed when creating or updating a conversation.

How Metrics Are Tracked

Your dashboard is populated based on how you track data. Metrics are grouped into three categories:

• Automatic Metrics - computed from system data (no extra setup).
• Conversation-Passed Metrics - require adding specific fields when creating a conversation.
• Specialized Metrics - based on specific actions or feedback.

You provide all contextual and specialized data by passing arguments to get_or_create_conversation (or initialize_conversation).

See the difference between these methods here.

def get_or_create_conversation(
    self,
    conversation_id: str,
    customer_id: Optional[str] = None,
    customer_ip_address: Optional[str] = None,
    device: Optional[Literal["desktop", "tablet", "mobile"]] = None,
    source: Optional[str] = None,
    language: Optional[str] = None,
    metadata: Optional[Dict[str, Any]] = None
)

Automatic Metrics

These metrics are calculated automatically just by using the core tracking methods.

Metric	Description	Tracking Method
Total Messages	Total number of messages exchanged across all conversations.	Automatically counted from all message records linked to conversations.
Total Conversations	Number of unique conversations started by users.	Automatically tracked via unique conversation_id values.
Average Bot Response Time	Average time the bot takes to respond after receiving a user message.	Calculated automatically using message timestamps.
Peak Hours	Highlights the most active times for user engagement by day and hour.	Derived automatically from message timestamps.
Messages & Conversations Overview	Daily comparison of total message volume and number of unique conversations.	Aggregated automatically from conversation and message logs.
Average Conversation Duration	Average time between the start (started_at) and end (ended_at) of conversations.	Computed automatically from conversation lifecycle timestamps.
Tool Usage and Average Duration	Breakdown of tool usage frequency and average execution duration.	Recorded automatically from action logs.

Metrics Passed with Conversation

To enable these metrics, include additional parameters when calling get_or_create_conversation() or initialize_conversation().

Metric	Description	Required Field / Tracking
World Map	Shows the global distribution of users initiating conversations.	customer_ip_address IP is automatically geolocated.
Device Usage	Breaks down interactions by device type (desktop, tablet, mobile).	device must be passed when creating the conversation.
Individual Users	Counts unique users interacting with the bot.	customer_id should be unique per user.
Source	Tracks which channel started the conversation (web, WhatsApp, Viber, etc.).	source must be passed when creating the conversation.
Language	Measures the language used in the conversation.	language must be passed when creating the conversation.

Specialized Metrics

These metrics are based on behavioral patterns or explicit user feedback rather than static conversation fields.

Metric	Description	Tracking / Data Required
Human Escalation Rate	Percentage of conversations escalated to a human agent.	Automatically detected from tool usage: ['fallback_to_human', 'open_ticket', 'ticket', 'contact_human'].
Conversation Feedback	Captures user feedback or satisfaction rating for a conversation.	metadata.feedback must be included in the conversation payload.
Unique Interaction	Tracks actual user engagement with the AI agent rather than just page visits.	Use initialize_conversation() first, then any subsequent call with the same conversation_id via get_or_create_conversation() counts as one interaction.