Automate Meta Ads work without automating away control

A useful automation starts with a condition the team understands: spend without a purchase, frequency above a limit, or return on ad spend sustained for a defined period. Adrails stores those thresholds as workspace context instead of burying them inside an opaque model response.

The agent can monitor campaigns against those rules and assemble the items that need attention. A human sees why each item was selected before deciding what should happen.

A recommendation identifies the campaign, ad set or ad, then shows its current state and proposed state. Approving one change does not approve every suggestion in the queue. Teams can accept, reject or edit the work at the level where spend actually changes.

This prevents a broad instruction from silently becoming a broad account mutation. It also makes reviews faster because the reviewer does not have to reconstruct the intended change from a conversation transcript.

• Pause or enable an entity
• Prepare budget adjustments
• Keep decisions inside an activity history

Deterministic rules remain appropriate for hard limits and schedules. An agent is better suited to assembling evidence, explaining tradeoffs and preparing a multi-step response. Adrails does not force every task into the same automation model.

Teams can keep critical guardrails deterministic while using agents for investigation, drafting and recommendations that need wider context.