How an AI agent can convert PostHog analytics signals and Linear product context into prioritized roadmap recommendations, issues, and experiment follow-up
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 PostHog context and matches it with Linear, 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 convert PostHog analytics signals and Linear product context into prioritized roadmap recommendations, issues, and experiment follow-up?
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.
What this agent helps you do
A PostHog and Linear analytics roadmap agent turns usage evidence into execution. PostHog supplies analytics, feature flags, experiments, surveys, replays, and error signals, while Linear supplies issues, projects, cycles, priorities, owners, and roadmap context.
When to use this workflow
Use it for growth reviews, product planning, experiment retrospectives, adoption investigations, or roadmap discussions where user behavior should inform priorities.
How PostHog and Linear give the agent context
Connect both plugins and choose the feature, funnel, experiment, or roadmap area. PostHog should show user behavior; Linear should turn insight into planned work without creating duplicate issues. Keep issue creation and status changes approval-based.
Example starter prompt
Investigate PostHog signals for this feature, compare them with related Linear roadmap context, and prepare issue recommendations with evidence, hypotheses, acceptance criteria, and priority rationale.Suggested workflow steps
Start with the analytics question and Linear project area. Have the agent inspect adoption, drop-off, experiment health, flags, surveys, or replays in PostHog, then search Linear for related initiatives and owners.
Questions this workflow answers
Which product problems in our usage data are already on the roadmap, and which need new investigation?
The agent begins with a product question and an agreed definition of the affected behavior. It might examine where a funnel changed, which cohort stopped using a feature, or whether an experiment exposed an instrumentation gap. Every observation keeps its project, query, date range, filters, denominator, and comparison period. A drop in events is not automatically a drop in user success if tracking, identity, or eligibility changed.
Once the evidence is stable, the agent searches roadmap projects and issues by affected workflow, feature, event, error, and acceptance criteria. It classifies relationships as direct, partial, adjacent, or absent. A planned redesign may overlap a weak adoption signal without addressing the cause. The output says which part of the observed problem existing work covers and which part remains untested.
New issue drafts start with the evidence and unknown, not a preferred feature. A draft can ask engineering to verify event emission, product to interview a cohort, or design to test a confusing step. It includes a reproducible query, expected learning, owner, and completion check. It does not convert correlation into priority or copy session details that expose customer data.
The review queue includes analytics findings with no action, existing work that needs better acceptance criteria, possible duplicates, and proposed investigations. Product owners decide importance alongside strategy, customer commitments, effort, and risk. They approve issue creation and project changes separately. After work ships, the original query and cohort provide a defined follow-up, which prevents a roadmap item from closing without checking whether user behavior changed.
Expected handoff
Ask for evidence, hypotheses, recommended issues, acceptance criteria, experiment follow-up, priority rationale, and approval-ready Linear updates.
The agent should match evidence to roadmap work by the product problem and affected behavior, not by shared keywords. A drop in invitation completion may support an existing onboarding project even if the issue never says “activation.” The handoff can link the funnel, cohort, date range, and segment to that issue, then state what decision the evidence changes. If no work matches, it drafts an investigation with a falsifiable question before proposing a feature. That prevents a surprising chart from becoming premature build scope.