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Analytics

Conversational Analytics

Conversational Analytics is generally available. It reads the conversations users have with your Product Agent and turns them into intent, resolution, gap, sentiment, and cost signals. Every conversation and tracked event flows in automatically once the agent is embedded, so there is nothing to configure beyond the agent itself.

Select Analytics from the top-left app switcher.

What users are trying to do

Section titled “What users are trying to do”

Path: Analytics → Dashboard → Conversations

User Intent classifies every conversation into what the user was trying to do:

IntentWhat it means
Information RequestThe user is asking for information.
Action RequestThe user wants the agent to do something.
Reported IssueThe user is reporting a problem.

A rising Action Request share means users increasingly want the agent to act rather than answer, which raises the value of closing action gaps. A rising Reported Issue share is usually a product problem worth investigating, not an agent one.

For a full time series, open Reports, add User Intent, and break it down by intent.

What the agent could not deliver

Section titled “What the agent could not deliver”

When the agent cannot deliver, the conversation is tagged, and the two gap tags are the actionable ones:

  • Agent Action Gap: users want a capability that does not exist yet.
  • Agent Knowledge Gap: the information was missing, stale, or never retrieved.

One is a build list. The other is a writing list. Both are ranked in aggregate on Signals frequency, and both are filters in Conversations so you can read the underlying requests.

In Reports, add the Agent Gap metric and break it down by Gap to see Action Gap against Knowledge Gap over time.

Whether the agent delivered

Section titled “Whether the agent delivered”

Path: Analytics → Dashboard → Conversations

Read the volume numbers as a set rather than in isolation. Conversations rising is ambiguous on its own, because retries after a failure also raise the count. Messages rising faster than conversations means users are working harder for the same result, which usually points to a knowledge problem rather than a capability one. Action calls rising alongside steady engagement is the healthiest pattern on the dashboard.

Actions by type, frequency, and cost

Section titled “Actions by type, frequency, and cost”

Path: Analytics → Event Explorer

Event Explorer is the catalog of every event and metric flowing into Foldspace, spanning product usage, conversational activity, and AI cost and token consumption. Because all three live in one place, you can see your key actions by type, frequency, and cost without leaving the surface.

Every metric is assembled the same way:

  1. Pick an event or metric, for example Action Called, which fires every time the agent performs a distinct task.
  2. Choose how to count it: unique conversations or users, or an aggregate (Sum or Average) of a numeric property.
  3. Filter and break down by any property.
  4. Chart it, or save it as a Report or a Dashboard widget.

Applied to actions, that gives you three readings of the same event:

QuestionHow to build it
Which actions run (type)Action Called, broken down by Action key.
How often (frequency)Action Called, counted as Total Events, as a stacked bar over 7 or 30 days.
What they costAction Called, aggregated as Sum of Estimated cost, broken down by Action key.

The most-called action and the most-expensive action are often not the same one, and the gap between those two rankings is usually where the optimization is.

  • A zero-usage action may have a bug, or the agent may not know when to call it.
  • A low-usage action may need better instructions.
  • Action Response Received confirms the action actually returned.

Cost and tokens Beta

Section titled “Cost and tokens ”

Path: Analytics → Event Explorer

Cost and tokens metrics work by aggregating the numeric properties on an event rather than counting occurrences. Select Action Called, open the counting dropdown, choose Aggregate Property, pick the property, and select Sum for the total or Average for the per-event figure.

You can aggregate Estimated cost, Action call estimated cost, Input tokens, Output tokens, Cached tokens, Thoughts tokens, Total tokens, and Action instructions tokens.

Break the total down by Action key to find the action driving spend, or by AI provider LLM model name or LLM provider to compare models and providers. Set chart type to Stacked Bar over 7D or 30D: each bar is that day’s total, and a large colored segment flags the driver.

Controlling and protecting spend

Section titled “Controlling and protecting spend”

Token Quotas cap what agents and users consume, with agent-global, monthly user, daily user, per-conversation, and burst limits configured per agent. Quotas count weighted tokens rather than raw messages, and the daily and monthly windows roll continuously instead of resetting on a shared date, so access returns gradually rather than in a spike.

High Availability is the uptime half of model routing. If a provider throttles or goes down, Foldspace fails over to the next healthy model and key automatically, usually without the user noticing. Unavailability applies to a single model, key, and region combination and never to a whole provider. Every failover and failback is recorded as a routing event in Event Explorer.

Path: Settings → AI Gateway

Reading the conversations behind the numbers

Section titled “Reading the conversations behind the numbers”

Session replay shows exactly what the user did on screen, with conversation events overlaid as a timeline. Reach for it when the transcript alone does not explain the outcome: an escalation, a thumbs-down with no obvious cause, or a strongly positive conversation on a valuable task.

In Conversations you can filter by tag, including Agent Action Gap and Agent Knowledge Gap, and read the requests themselves. You can also ask the built-in Foldspace agent there, for example “What are the action gaps in the last 30 days?” or “What topics get the most thumbs down?”, then apply the matching filter and read the conversations yourself.

Who is using the agent

Section titled “Who is using the agent”
  • Audience lists every identified user, with last seen, sign-up, subscription, role, and device. Open a row for that user’s conversation history, replays, and attribute timeline.
  • Segments define named groups by attribute or behavior. Once saved, a segment becomes a filter across Dashboard widgets, Audience, and Reports.
  • Product analytics covers new and active users, subscriptions, and retention, built from what the SDK captures.

Call foldspace.identify() to see named users and segments rather than anonymous sessions.

Get started

Section titled “Get started”
  1. Open the Dashboard. The default widgets already track conversations, resolution, handoffs, sentiment, action calls, languages, and signal frequency.
  2. Read the quantitative layer first. Conversations, User Messages, and Action Calls, as a set.
  3. Build the Agent Gap report. Add Agent Gap, break down by Gap.
  4. Build the intent breakdown. Add User Intent, break down by intent.
  5. Check actions in Event Explorer. Break Action Called down by Action key for frequency, then aggregate Sum of Estimated cost on the same event for spend.
  6. Watch a few escalated replays. Filter Session replay to sessions with a handoff.
  7. Re-check next period. If a gap’s share dropped, the fix worked. If it held steady, read the new ones.
Section titled “Related”