How did we solve this before

Every time an AI session finishes a piece of work, it can log what happened: the area it touched, the action it took, the files involved, and a short summary. On its own, each log is a single line in a long list. Work episodes turn that list into something a future session can actually use.

Pathrule rolls related activity from a working session into a work episode: a titled record of one thing your team solved, with the subjects it was about, the paths and files it touched, the AI clients involved, and when it happened. Months later, when someone hits the same area again, that episode is there to recall.

Recall is on demand, not on every prompt

Work episodes do not ride along in every request. An ordinary prompt adds no history at all, so your context window is not paying a tax for memory you are not using.

When your agent is doing the kind of work where the past matters (fixing a bug, debugging, refactoring, or exploring an unfamiliar area) Pathrule can include a few of the most relevant prior episodes in the context it returns. Your agent can also ask for prior work explicitly, and a strongly worded historical question ("how did we handle this last time") is enough to trigger a look. For small UI tweaks and brand-new features, episodes stay quiet, because anchoring fresh work to old patterns tends to cost more than it helps.

What makes an episode relevant

Relevance is computed from two plain signals and one preference, not from a guess:

• What the work was about. An episode about authentication is a candidate when you are back in authentication, not when you are formatting a table.
• Where the work happened. An episode that touched a path is a candidate when you are working at or under that path, in either direction.
• How recent it was. Recent work is favored over stale work, because code drifts and old episodes describe a codebase that may no longer exist.

The result is a short, ranked set: a handful of episodes at most, each compact enough to read at a glance.

Skip beats wrong

Pathrule would rather surface nothing than surface the wrong thing. If an episode has no real overlap with what you are doing, it is not shown. If the system is not confident, it stays quiet. If your workspace has not built up enough history yet to score reliably, recall short-circuits and returns nothing. You will not see a wall of vaguely related work; you will see the few episodes that earned their place, or none.

Built from what you log, never from your code

Work episodes are built only from the activity your agents log through Pathrule. Pathrule does not read, scan, or index your source files to build them. The same access control that governs the rest of your workspace governs episodes: they are scoped to your workspace, behind your team's per-user access, and identical for everyone who can see them.

Episodes that keep getting used stick around. Episodes nobody returns to age out on their own, so the history stays a record of work that mattered rather than an ever-growing log.

Where to find this

Work episodes surface through the context your AI client receives, on the kinds of tasks where they help. You do not configure them per repo and you do not turn them on prompt by prompt. They are a property of a workspace that has accumulated real, logged work. See Suggestions and self-audit for the related maintenance signals Pathrule raises about your stored memories and rules.