Agents

A proactive teammate that runs work end to end. Nothing ships without your sign-off.

Priori Runner puts AI agents on the context layer and in your tools. They watch on their own, propose work before anyone asks, and carry a task from draft to done with every step logged. This page explains what the agents do, where they run, and how you stay in control.

Priori Runner

A standing task on a clock: the instruction, the schedule, the run history with outcomes, and a journal thread you can open and read.

Why chat AI is not a teammate

It waits to be asked

A chat assistant does nothing until someone types. The anomaly at 2am, the payout that needs checking, the report due Monday: none of it starts on its own.

It starts from zero

Ask about MID-7 and it does not know what a MID is, which one is quarantined, or what was decided about it last month. Every conversation rebuilds context by hand.

It cannot finish the job

It drafts the text and stops. Opening the PR, updating the dashboard, watching the A/B test, filing the result: the last mile stays with you.

What Runner is

Runner agents read and write the context layer, so they know what a sub-ID is, which affiliate it belongs to, and what happened to it last week. Context carries across conversations because it lives in the graph, not in the chat history.

They are proactive. Agents run on schedules and react to events: a Sentry alert, a month-end close, a new affiliate landing in the CRM. Work starts before anyone asks, and the result arrives in Slack where the team already works.

What it runs

The work runs from operations to development, carried end to end. Month-end reconciliation across the processor and the tracker. A dashboard built from a one-line request. Support triage with the customer's full object history attached. A payout check before the money moves. An offer page drafted from an affiliate manager's idea. A feature drafted, opened as a PR, and watched through the test.

Results write back into the context layer. The reconciliation report, the dashboard, the triage decision all become part of the model, so the next task starts from what the last one learned.

A revenue dashboard the agent built and then edited from chat — Request to result, no handoffs: two chat messages in, a finished revenue dashboard, built and then edited in place by the agent.

Control

Nothing acts without sign-off. An agent proposes, you approve, it executes. Scopes decide what an agent may touch, and every step lands in an audit log: what it read, what it did, what it produced. Reviewing an agent's work is reading a record, not reconstructing one. Permissions come in layers: each agent declares a baseline, you grant standing permissions that hold until you revoke them, and anything not granted suspends the run and asks. Where you are watching, it never needs to ask, because every write is a version you can inspect and undo.

The permissions page: agent defaults, standing grants with revoke, and the grant form — The permission ledger: what each agent declares, what you granted, and the revoke that takes it back.

The action-by-action record of one agent run in chat — The full trail of a single run: every query it ran and every chart it previewed, each one openable, in the order it happened.

So what?

Work starts without a prompt. Schedules and events kick off the routine tasks; you see proposals, not blank chats.
Context persists. Agents know your objects and your history, so nobody re-explains the business every morning.
The loop closes. Draft, execute, verify, file. Tasks end done instead of ending as a draft.
Every action is reviewable. Sign-off before, audit log after. Autonomy without the black box.

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