Working with autoresearch

Autoresearch answers one standing question per model: who in a population will do a target within a horizon? For example, "which active users will open a report in the next 7 days?".

1. The user names a target (an event or a PostHog action), a population and a horizon in days.
2. A training agent runs in a PostHog sandbox. It writes feature SQL and a model, scores each experiment on a holdout set, keeps what improves holdout AUC and discards the rest.
3. The server picks the champion from the kept experiments. The agent proposes; the backend decides.
4. Every cadence (daily by default, at 02:00 UTC), the champion scores the inference population. Each scored person gets an autoresearch_prediction event with their probability. A person who resolves to a current distinct ID also gets the model's output person property, for example predicted_p_adopt_insights_14d.
5. When a prediction date's horizon has passed, online validation checks the scores against what really happened and records realized AUC.

In the PostHog UI the entity is a model. The API and tools call it a pipeline. Use "model" when you talk to people.

Autoresearch is behind the autoresearch feature flag. Training runs in a cloud sandbox, so it also needs PostHog Desktop access. If the autoresearch-* tools are missing, the project probably does not have access yet.