How an AI agent can diagnose a conversion-funnel change with reproducible queries and competing explanations
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 Mixpanel 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 diagnose a conversion-funnel change with reproducible queries and competing explanations?
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.
Make the funnel definition explicit
A Mixpanel funnel agent helps investigate a specific conversion question: where did onboarding completion change, or which step lost the most users after a release? Start with the project, population, ordered event sequence, date range, timezone, and conversion window. Reuse an approved saved report when it captures the intended definition instead of recreating a similar-looking funnel with different settings.
Discover event names and properties before querying. Display names may hide different underlying events, and a property can be missing for part of the population. Mixpanel’s MCP documentation describes project and event discovery, query schemas, analytics queries, and saved reports. Inspect the connected tools and permissions; do not assume that every feature listed upstream is present in this connection.
Check measurement before explaining behavior
Compare step counts and denominators, not only conversion percentages. Keep the event order, identity basis, filters, and attribution assumptions stable across periods. Recent entrants may not have had the full conversion window to finish, so either use mature cohorts or label the comparison as incomplete. Differences in weekday mix or acquisition channel can also change the apparent result.
Review instrumentation changes, event duplication, missing properties, and identity transitions where evidence is available. A renamed completion event can resemble a product failure. Ask for release context if the analytics connection cannot supply it. A report should separate a measured change from hypotheses about friction, traffic quality, or tracking errors.
Example starter prompt
Investigate the change in [saved Mixpanel funnel or event sequence] for
[project]. Compare [complete baseline period] with [complete comparison period]
using [timezone, population, and conversion window].
Discover the relevant events and properties and inspect the query schema first.
Preserve the funnel definition. Return step-level counts, denominators, conversion
rates, absolute changes, and supported breakdowns with segment sizes.
Check for missing events, identity differences, incomplete conversion windows,
and known instrumentation changes. Separate observations from explanations and
list queries needed to distinguish them. Prefer aggregate data. Do not edit
events, change dashboards, create cohorts, or launch an experiment.Investigate the largest credible change
Begin with the overall funnel, then examine a few relevant breakdowns such as platform or acquisition source when those properties are available and meaningful. Keep a minimum sample rule chosen by the team. A dramatic percentage movement in a tiny segment should not outweigh a smaller change affecting a substantial population. Explain whether a breakdown is exhaustive or contains an unknown category.
Retain the saved report reference or query definition, collection time, filters, and comparison settings. The final diagnosis should show what changed, which evidence supports that observation, what might explain it, and what evidence is missing. Avoid including individual user identifiers when an aggregate is enough. Session-level investigation, if needed and permitted, is a separate scoped step rather than a reason to copy private behavior into a public ticket.
A useful handoff may recommend fixing measurement before changing the product. If the evidence supports a product hypothesis, the Mixpanel and Linear guide shows how to turn it into reviewed work. No chart alone proves that the latest release caused a conversion change.
Questions this workflow answers
What should be checked before blaming a conversion drop on a product release?
First establish that the two numbers describe the same population and measurement rules. Compare event names, order, conversion window, timezone, filters, identity handling, and the time available for entrants to complete the journey. Then inspect raw step counts and the size of each denominator. A percentage can fall because the traffic mix changed, because more people entered an early step, or because a completion event stopped firing, even when the experience itself did not become worse.
Review plausible measurement changes alongside product changes. Ask whether a new SDK, renamed event, login transition, or missing property affected the report. Compare mature periods and preserve unmatched or unknown segments rather than dropping them silently. Only after those checks should the investigation rank behavioral explanations. State what additional observation or controlled test would distinguish them. A good result can be “the conversion metric is not yet comparable” with a precise instrumentation task. That is more useful than a confident story about a release based on a chart whose definition changed underneath it.