Analytics

Analytics tells you how your agent is doing in production. It covers the volume of conversations handled, the share the agent resolved without escalation, why conversations escalated when they did, how response time is trending, and what customers thought of the replies. This is the page you read when a stakeholder asks “is the agent working?”, when you shipped a change and want to confirm it didn’t regress deflection, or when you’re prioritizing knowledge work and need to know which intents drive the most volume and the most failures. Everything on the page sits on one scrolling surface. The header keeps the agent, section shortcuts, date range, and filter row visible while you scroll, so you can move between the major Analytics sections without losing the current scope.

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Global controls

Most controls at the top of the page filter everything below them. The small Today pulse in the header is the exception: it always shows today’s live conversation count and deflection rate for the selected agent, independent of the selected date range and filters.

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Agent picker

Top-left, beside the page title. Scopes every chart and table to the selected agent. Multi-agent workspaces use this to isolate a regression to the agent that caused it.

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Date range

Defaults to the last 7 days. Pick any custom window for spot analysis or longer-term trends. The selected range applies to every KPI card, chart, and table below the header. KPI change pills compare the selected window against the immediately preceding window of the same length.

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Filters

A row of filter pills sits under the page header. Core filters are pinned by default; optional filters live under the + Filter dropdown. Filters compose, so combining Channel = email and Sentiment = negative isolates angry customers on the email channel specifically. Use Views to save a filter setup you reuse, such as a weekly executive slice, a channel-specific review, or one team’s intent-rule view. Saved views store filters only. Applying one keeps the current date range, so you can reuse the same slice across different reporting windows.

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KPI cards

Five cards across the top summarize the period. Each shows a value and a colored pill indicating change versus the previous period of the same length. Average CSAT reads NA when CSAT collection isn’t configured for the agent, or when no conversations in the range carry a rating. The card, the CSAT column in Knowledge performance and Intent rule breakdown, and the CSAT filter all populate once ratings start coming in. See CSAT and sentiment for how ratings are captured.

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Trends

Conversation trend charts sit below the KPI cards.

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Conversation volume, Resolution rate, and CSAT

A toggle at the top of the first chart switches between trend views over the selected range:

• Conversation volume, total conversations per day. Use it to spot traffic spikes and drops.
• Resolution rate, the daily resolution rate plotted alongside the Waiting for Customer share. A drop in this trend right after a knowledge or prompt change is a strong signal to roll back or investigate.
• CSAT, daily customer satisfaction trend when CSAT data is available.

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Average response time

A dedicated chart plotting median response time per day across the same window. Use it to catch latency regressions, a steady climb often traces to a slow Action the agent calls mid-conversation, which the response-time trend surfaces before it shows up in CSAT.

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Distribution

The distribution section breaks the period down by channel and by status, then shows channel-level detail below the charts.

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Usage by channels

Conversation volume split by channel, typically Chat versus Email. Useful for sanity-checking that traffic lands on the channels you expect. A sudden shift from chat-dominant to email-dominant can mean the chat widget broke.

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Conversation Status

The three conversation statuses, Resolved by AI, Escalated to Human Team, and Waiting for Customer, as proportions of total volume. This is the visual form of the three statuses sum to 100% model from the Note above.

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Channel breakdown

The channel table sits below the doughnuts. It shows volume and quality by source channel, useful when one channel regresses while overall performance looks flat.

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Hourly breakdown

For ranges up to 31 days, Analytics shows volume by hour and resolution rate by hour. Use this to spot staffing or routing gaps: a healthy daily average can still hide a specific hour where resolution falls off.

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Escalations

When the agent escalates, this section tells you why. Read it when deflection rate is too low: the KPI cards tell you that it’s low, the escalation breakdown tells you what to fix.

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Escalation reasons

Every escalated conversation is tagged with a reason from a fixed taxonomy. The reasons group into four families. Knowing which reasons drive escalation tells you exactly where to spend effort.

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The escalation doughnut

The center count is the total number of conversations in the period, not just the escalated ones. The largest segment, Not escalated, covers everything the agent resolved without escalating. The remaining segments break the escalated conversations down by reason, colored by family. Big non-grey segments are your top fix-it targets.

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CSAT and sentiment

Two feedback signals run alongside each other across the page: what customers explicitly said, and what their words imply. The two often diverge. A customer might end on a thumbs-up after a frustrating multi-turn exchange (high CSAT, low sentiment), or get a fast answer and never bother rating it (high sentiment, no CSAT). Reading both is more honest than either alone. Where each signal shows up on the page: CSAT drives the Average CSAT card, the CSAT trend, the CSAT columns in Knowledge performance and Intent rule breakdown, and the CSAT filter. Sentiment is available as a filter under + Filter and informs the per-conversation view in Inbox. When CSAT isn’t configured, the CSAT surfaces read NA and sentiment carries the feedback signal on its own.

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Breakdowns

Two tables break the period down: one by knowledge usage, one by intent rule.

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Knowledge performance

A table breaking conversations down by knowledge usage. It shows which folders, articles, or source groups the agent used and how each slice performed. For each row, the table shows:

• AI Resolve Rate, share of conversations in that row the agent resolved.
• Escalated Rate, share that escalated to a human.
• CSAT, the CSAT rate for that row, as a color-graded pill. Reads NA when CSAT isn’t configured.
• Volume, total conversations, with a pill showing change versus the previous period.

This is one of the most actionable views on the page. A high-volume knowledge slice with a low resolve rate is your highest-leverage target: it tells you what to fix in Knowledge next. The arrow on the right of a row opens the conversations behind it in Inbox, useful for spot-checking what went wrong. The CSAT pill uses three color bands, so a row that reads red across CSAT is a clear fix this first signal:

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Intent rule breakdown

A table breaking performance down by Rulebook intent rule, the rules that decide how the agent handles each class of request. Columns:

• Intent rule, the rule name as it appears in your Rulebook.
• AI Resolve Rate, share of conversations the rule handled that resolved without escalation.
• Escalated Rate, share that escalated.
• Volume, total conversations the rule handled.

A rule with a low resolve rate and a high escalated rate is leaking conversations to humans, look at whether it’s missing an Action, missing knowledge, or gated too tightly. This is the live, production counterpart to the regression results in Test Suite: Test Suite tells you whether a rule passes its tests, this table tells you how it behaves on real traffic.

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Common workflows

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Weekly health check

1. Set the date range to the last 7 days.
2. Glance at the KPI cards; compare deflection rate to last week.
3. Check the Resolution rate trend for sharp drops; correlate with deploys or knowledge changes.
4. Skim Knowledge performance and Intent rules for any row that regressed; use the row arrow to open those conversations and spot-check.

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Pre-release validation

1. Set the date range to the period since you started rolling out a change (for example, the last 24 hours after a prompt edit).
2. Compare deflection rate, Average response time, and Average CSAT against the prior period.
3. If any worsened, roll back. If all are flat or better, continue rolling out.

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Knowledge prioritization

1. Open Knowledge performance. Find a knowledge slice with high volume and low AI Resolve Rate.
2. Filter by Escalation reason for that slice and see which reasons dominate.
3. If Missing Knowledge leads, add Articles or Sources. If Ambiguous or Unclear Input leads, tighten the Rulebook.
4. Check the same intent the following week. AI Resolve Rate should rise and the escalation reason should shrink.

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Diagnosing a CSAT drop

1. Apply the CSAT filter, or sort Knowledge performance and Intent rules by CSAT, to find which slices drove the drop.
2. Compare CSAT against sentiment. If both fell together, customers genuinely got worse outcomes, go to the escalation breakdown to see why.
3. If CSAT fell but sentiment held, something about how the agent communicated changed, check recent Prompts edits.
4. Open the affected conversations in Inbox and read ten of them to find the pattern.

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What’s not in Analytics

A few things this page doesn’t surface; you go elsewhere for them:

• Per-conversation debugging, that’s Inbox, with the full AI Steps trace per conversation.
• Test pass rates, whether an intent rule passes its regression tests, that’s Test Suite. The Intent rule breakdown above shows live resolve, escalate, and volume per rule; Test Suite shows pass or fail against your test cases.
• Cost and token usage, tracked in workspace settings, not in Analytics.

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