Your team stops prompting one message at a time. You build loops where agents run, learn, and deliver outcomes — in the same workspace as the humans who guide them.
In the AI-Native Loop Sprint, you set up your first self-learning loops in half a day to a day, then run them alongside our team on Slack for the rest of the sprint. Every loop learns from what it just shipped, so quality climbs while you watch. By the end, your loops have graduated from dry run to either semi-autonomous or fully autonomous — delivering outcomes on their own, with your team stepping in only for the calls that matter.
Your team learns to build loops that keep running — the same way you'd write a playbook, but the agents run it, not a person.
Every agent output is context every next agent inherits. No re-briefing, no restarting from zero, no dropped context between roles or hires.
Self-host on your own infrastructure. Bring your own model. Apache 2.0. No lock-in.
Humans set the outcome and gate the calls that matter; agents produce against the standard forever.
Seven loop shapes teams start with in a sprint. As the marketplace fills out, each row links to its own listing.
Bring your own loop shape too — any weekly bottleneck your team keeps hitting is a loop candidate.
You name the weekly bottleneck. We agree which loops the sprint will set up.
Half a day to a full day — we set up the loops with your team, on your stack, with your models.
Your team has direct Slack access to ours while the loops run. We hold follow-up sessions to review outputs, tighten close conditions, and lift the loops out of dry run.
Every loop has graduated to semi-autonomous or fully autonomous — based on what it's learned during the sprint — and is delivering outcomes with your team stepping in only for the calls that matter.
Every loop starts in dry run — producing under supervision, every output critiqued.
When the loop's quality holds against its close condition, it graduates to:
The loop runs on its own, but every material call goes through a human gate before it lands.
The loop runs on its own end-to-end, with a human sampling outputs and stepping in when the close condition drifts.
Loops that stop meeting their close condition get demoted back to dry run automatically. Trust is earned per loop, on evidence — not on a calendar, not on a demo.
Setup is half a day to a full day. The sprint window runs after that, with Slack access and follow-up sessions until every loop has graduated. We scope the exact length on the intro call.
Named on the intro call, tailored to the sprint scope. We don't publish a rate because the scope moves with your ambition.
Yes. Apache 2.0. You run it on your own infrastructure and pay your model provider directly.
Yes. Model-agnostic. Route each loop to the model that fits.
Any of the seven above, or a shape you bring us. Common starts: Product Discovery to Strategic Bets, one go-to-market loop, and Shared Business Context underneath, all feeding each other.
It stays in dry run and we keep tightening it in follow-up sessions. Loops that never earn their close condition get retired — you keep the substrate and the learnings.
No. Loops can ship inside Maskin with coding agents, or hand off to Linear or Jira. The sale is the layer above your stack, not the tracker.
A consultant leaves you with a slide deck. We leave you with loops that are still running when we're gone, and a team that can build the next ones on their own.
Limited slots. Book a meeting or email us to check current availability.