Execution Agent

Not a recommendation. A change.

Budget shifts, creative swaps, whole campaigns from scratch, shipped live to Google, Meta, TikTok, and LinkedIn. Inside your guardrails, with one-click.

The Loop

Next Steps

Signals In

Proposed changes from every other agent, each with the action, the signal behind it, and the agent's confidence.

Your guardrail policy: volume limits, spend caps, blast-radius rules, auto-approve thresholds, set per workspace.

The approval queue: what's pending, who approves, the SLA.

Example: the MMM flags Meta headroom and proposes a $40K/week budget increase.

Actions Out

Changes shipped on-platform. Shift a budget, build a campaign from scratch, swap a fatigued creative, pause a stale audience, written straight to Google, Meta, TikTok, and LinkedIn.

Example: the agent raises Meta budget $40K/week, live and inside your caps, with one-click rollback.

A change log: who proposed each change, why, who approved it, and how to reverse it. Queryable in plain English.

An approval queue: pending changes with full context, shipped changes with one-click rollback.

Questions

What teams ask us first.

  • Why is this safer than letting a platform optimise on its own?

    What if an agent recommends something dangerous?

    Does the agent ever take an action without explicit approval?

  • Why is this safer than letting a platform optimise on its own?

    What if an agent recommends something dangerous?

    Does the agent ever take an action without explicit approval?

Stop copying recommendations into ad manager. Start shipping changes, safely.

30-minute walkthrough on your real ad accounts. We'll connect, set the guardrails together, ship one small change live, and roll it back — so you see the full loop.

Stop copying recommendations into ad manager. Start shipping changes, safely.

30-minute walkthrough on your real ad accounts. We'll connect, set the guardrails together, ship one small change live, and roll it back — so you see the full loop.