When does an AI agent use this pattern?

When the trigger described above matches the situation the agent is handling. The agent queries FusedFrames at runtime, this pattern is returned and the agent follows the recorded reasoning and steps to reach the recorded outcome.

How were the steps captured?

By the desktop app on the machine of someone who actually does this work. It records clicks, keystrokes, copy and paste, drag and drop, selected text and screen regions across Gainsight, then groups them into the meaningful steps shown above.

Where does the reasoning come from?

From the operator. When the desktop app sees a decision the action alone cannot explain, it asks why in the moment. The operator's answer is recorded and becomes the reasoning attached to the pattern, so the AI agent gets the thinking and not just the steps.

What if my team handles this differently?

Capture how your team actually does it. The desktop app records what your operator does on your tools and the web app distills that into a pattern shaped by your team. Each team's library reflects their own approach rather than the example shown here.

What happens when a case looks like this pattern but does not quite fit?

The connections shown on this page link to related patterns that may match the case better. The agent can follow those edges to find a closer fit.

How does my AI agent actually call this pattern?

Through the read-only REST API or the npm CLI. Create an API key in the dashboard, choose whether it accesses all libraries or specific ones and have your agent query at runtime. The response is structured JSON ready for tool use.

Is the data shown on this page from a live customer?

No. Example libraries on this site are illustrative. Patterns in your own account are built from your team's captured actions and stay private to your workspace.

How do I build a pattern like this for my team?

Install the desktop app on the machine of the person who already does this work, ask them to handle a real case end to end, then distill the captured action into a pattern in the web app. The pattern you end up with mirrors how your team actually operates.

Building a pre-QBR brief

Five working days before each QBR, the CSM captures the structured half of the brief in Gainsight. They walk through the 90-day health trend, sponsor changes, NPS verbatim and adoption metrics, pasting each finding into a named C360 activity note ready for the qualitative half to be added.

Tags

qbrgainsightc360sponsor-trackingpreparation

Lets your agent filter to the right pattern

Categories and tags scope the retrieval set, so your agent receives only the patterns relevant to the task instead of scanning the full library.

What and why

The observed behaviour and the reasoning behind it.

Behaviour

Five working days before a QBR, the customer success manager opens the customer’s C360 in Gainsight from the auto-generated ‘QBR Prep’ CTA, sets the health timeline to 90 days, screenshots the trend chart and walks through the Sponsor Tracking widget, the latest NPS verbatim and the adoption metrics in turn. They paste each finding into the structured Gainsight ‘QBR Brief’ activity note on the C360, with named sections for Trend, Sponsor changes, NPS, Adoption and Open CTAs.

Reasoning

QBRs hinge on the story the team can tell about the last quarter, and the story has to start from the data the customer themselves see when they log in. The 90-day window is exact, not ‘last quarter’ or ‘since last meeting’, because customer execs see calendar quarters and a 90-day rolling window catches changes that happened right after the previous QBR. Sponsor Tracking matters more than any single score. A champion change in the last 90 days completely reframes the meeting from ‘how is it going’ to ‘who are we working with now’. Pasting into the structured activity note (rather than freeform notes) means the next prep step (mining Gong calls) does not have to re-establish anything.

Gives your agent an operator's reasoning

Behaviour records the action; reasoning records the rationale. Your agent applies both to handle cases that diverge from the captured examples.

Cause and effect

What initiates this pattern and what it produces.

Trigger

A QBR is on the calendar within five working days, indicated by the auto-fired ‘QBR Prep’ CTA on the Cockpit.

Outcome

The ‘QBR Brief’ activity note on the customer’s C360 is half-filled with structured Gainsight findings, ready for the Gong call mining step to add the qualitative half.

Tells your agent when to act and what to verify

Your agent stays within the scope set by the trigger and outcome, so it doesn't extend beyond the work or finish before the outcome is met.

Standard operating procedure

Step-by-step instructions to reproduce this pattern.

Gainsight

Open the customer’s C360 by clicking the customer name on the auto-generated ‘QBR Prep’ CTA.

The CTA fires automatically five business days before any QBR booked in the Gainsight calendar. Going via the CTA (rather than searching the customer name) attaches every action you take in the next twenty minutes to the CTA timeline, which makes the prep auditable later.

Expected: The customer’s C360 loads with the Health Scorecard tab selected and the QBR Prep CTA pinned at the top.

Gainsight

Switch the health timeline window to ‘90 days’ using the dropdown above the trend chart.

The default is 30 days, which always misses the post-QBR rebound or the dip from the previous quarter. 90 days is the calendar quarter the customer’s exec sees in their own dashboards and is non-negotiable for a brief.

Expected: The trend line redraws across 90 days and shows quarterly inflection points clearly.

Gainsight

Take a screenshot of the trend chart with Cmd+Shift+4, drag-select the chart and paste it into the Trend section of the QBR Brief activity note.

A screenshot rather than a Gainsight share link is deliberate. The screenshot ends up in the QBR slide deck verbatim. A share link would assume the customer has Gainsight access, which they do not.

Expected: The Trend section of the QBR Brief contains the chart image plus a one-line note on which component drove the biggest movement of the quarter.

Gainsight

Open the Sponsor Tracking widget and check whether any sponsor has changed in the last 90 days.

A sponsor change is the single biggest signal in any QBR. If the primary sponsor has changed, write ‘NEW SPONSOR · restart the relationship arc’ at the very top of the QBR Brief. The Gong mining step that follows will look for the new sponsor’s first call and use that as the meeting opener.

Expected: Sponsor history is captured. Any change in the last 90 days is flagged at the top of the QBR Brief in capitals.

Gainsight

Open the NPS widget, paste the most recent score and the verbatim feedback into the NPS section of the QBR Brief.

Always paste the customer’s verbatim feedback, even when it is harsh. Sanitising it in the slides makes the meeting feel canned, and customer execs notice. If the verbatim is brutal, that is the strongest opening you have for an honest QBR.

Expected: The NPS section contains the latest score, the change vs the previous quarter and the customer’s exact words.

Gainsight

Scroll to the Adoption section of the C360 and capture licence utilisation, weekly active users and the feature-usage trend.

The trend matters more than the absolute. A flat line on a small base is not a problem. A downward line on any base is. Paste both the numbers and a one-line interpretation into the Adoption section.

Expected: The Adoption section contains licence utilisation, weekly active users, the feature-usage trend and a written interpretation.

Gainsight

Open the Open CTAs list and write each CTA title, owner and age in days into the Open CTAs section.

Customers often ask in the QBR ‘is anything still open on your side’ and the worst answer is ‘let me check’. Listing them in the brief means you can answer with specifics in seconds, which builds trust faster than any slide.

Expected: The Open CTAs section is filled with the title, owner and age in days for every open CTA on the account.

Hands your agent your team's exact steps

Your agent executes the documented procedure in order, against the applications named at each step, producing consistent output across human and agent runs.

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Related patterns

How this pattern connects to other patterns in the library.

Often previous

Triaging daily account health

Often next

Mining Gong for QBR talking points

Lets your agent confirm the match and its scope

Alternative patterns let your agent compare candidates and select the closest match. Previous and next patterns define position in the workflow, so the agent operates within the scope of this one.

Supporting actions

Actions that provide evidence for this pattern.

Set Monarch Foods C360 timeline to 90 days; captured trend screenshot for Q2 brief

Flagged sponsor change for Juniper Retail (Priya Patel replaced David Wong)

Pasted Q1 NPS verbatim from Helios Energy into QBR Brief activity note

Listed 4 open CTAs (avg age 11 days) on Cascadia Bank QBR Brief

Shows your agent the evidence behind the pattern

Each pattern is derived from captured action sequences. Your agent can reference the source actions to verify any field, so nothing it acts on is authored by hand or hallucinated.

Metadata

Timestamps and identifiers.

Evidence	Observed 28 times across 5 connections
Applications	Gainsight
First seen	22 Jan 2026, 13:11
Last seen	1 May 2026, 15:42

Helps your agent rank patterns by confidence

Action count and last-seen timestamp weight each pattern by frequency and recency, so your agent selects the highest-scoring match rather than the first returned.

Questions

Frequently asked questions

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