How an AI agent can produce a product feedback brief that preserves distinct requests
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant Pendo context, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent produce a product feedback brief that preserves distinct requests?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
Keep source feedback visible
A Pendo feedback analysis agent reviews a selected set of responses for one product area and period. Give it the taxonomy the team uses, any account or user segments allowed in the analysis, and the question the review should answer. It can group similar problems while retaining the source record, wording, date, and account context.
The agent should separate what the user reported from the team’s interpretation. “I cannot find export” may indicate discoverability, permissions, missing functionality, or a support question. The evidence does not choose among those explanations by itself.
Example starter prompt
Analyze Pendo feedback for [product area] between [start] and [end]. Use these categories: [taxonomy].
For each theme, include the number of source records, representative examples, affected segments when available, contradictory feedback, and questions that need follow-up. Link every conclusion to the Pendo feedback records behind it.
Do not assign priority, contact users, or edit feedback. Return themes, outliers, and a source register.Preserve minority and high-impact reports
Review outliers separately from the largest clusters. A rare report from a key workflow can require attention even when it does not form a large theme. Conversely, repeated vague requests may still need interviews before they support a roadmap decision.
Questions this workflow answers
What are customers repeatedly struggling with, and which complaints only look similar at first glance?
An agent can read a bounded set of feedback and produce a problem map without turning every mention of the same noun into one theme. It starts by recording the user’s wording, the product area, date, account or segment context that the team is allowed to use, and any linked page or feature. It then groups reports by the blocked job. “Export is broken,” “I cannot find CSV,” and “the downloaded file omits filters” all mention export, but they describe reliability, discovery, and data-correctness problems that need different follow-up.
For each proposed theme, the agent shows the inclusion rule and a few representative records. It also lists near-matches it excluded and explains why. That gives a product manager a way to challenge the grouping instead of accepting an opaque topic label. Counts include a denominator and the review window, so twelve reports are not presented as “most customers” when the source set contains thousands of unrelated records. Repeated submissions from one account can be counted as several incidents and one affected account, with both numbers visible.
The analysis should preserve tension in the source material. Some users may want more defaults while others need granular control. A small number of detailed reports may reveal a severe workflow break even when a larger cluster describes a mild inconvenience. The agent can flag those cases for interviews, support review, or usage analysis without assigning roadmap priority.
The final brief gives the team themes, source links, affected jobs, segment notes, exceptions, and open questions. A reviewer can split or merge themes and record the reason. That edited taxonomy becomes the comparison point for a later review, which makes changes in feedback easier to distinguish from changes in classification.
The final brief should state the review scope and avoid extrapolating beyond the selected records.