Reef — the control plane
for your AI agent fleet.
Writing code stopped being the bottleneck — managing it did. Reef is the operational platform for teams that build software with fleets of AI agents: an idea becomes a plan on the board, the board dispatches cloud agents, agents come back with pull requests — and every dollar of it is visible the moment it is spent.
One loop, from idea to merge
PM, developers and cloud agents work in a single loop. A human steers; the fleet executes. Nothing disappears into a black box — every step is visible, priced, and reversible.
Idea
Bring a rough thought. Triton — the AI composer — pressure-tests it with you in a chat, against the real codebase.
Plan
The dialogue becomes an editable draft: epic, tasks, subtasks — reviewed, corrected, and approved on the kanban board.
Execute
Cloud agents pick tasks up and work autonomously in sandboxes. Live status, resources and spend tick on each card.
Pull request
Agents come back with a ready pull request. Auto-review flags what actually needs your eyes.
Merge
You approve — the loop closes. The board, the git history and the cost ledger all agree on what just happened.
$ the economics are visible at every step — per session, per task, per epic, the moment they occur.
Born in the MacBook notch
Reef started in the notch and lives there on your desktop — the fleet's glanceable readout at the top of the screen. Below is a tiny Mac running a tiny Reef. It works: click the island, press A, drag the cards, switch the tabs.
Bash · npm run deploy
Drag a card into In progress — an agent picks it up and the meter starts. Click a card to move it along.
The notch is the door. Behind it — the platform.
A full-size workspace, a cloud command center, a meeting assistant, an AI product team, spend analytics, organizations and roles — one operational platform, one design language.
Cloud command center
Every cloud agent session live: per-agent breakdown, CPU and memory curves, session replay — and a stop button that always works.
runs · resources · replayDeep Mode workspace
The notch opens into a full workspace: up to four sessions side by side, file tree, diffs, terminal, git — a post-IDE day without the IDE.
sessions · git · terminalSonar, in your meetings
Detects the call, transcribes on your Mac, keeps live notes and follow-ups, and turns decisions into board tasks — on-device by default.
on-device · notes → tasksCoral — an AI product team
A product owner for your board: watches dependencies, priorities and risks, runs discovery, and proposes what to build next — you keep the veto.
discovery · priorities · risksEconomics, live
Every session, task, run and epic wears its price the moment it occurs. Burn-rate, budgets, forecasts and per-model analytics — no end-of-month surprises.
tok · $ · budgets · forecastsOrganizations & roles
Projects, teams, access and sandbox limits. Developers see their sessions; leads see the team; finance sees the forecast.
projects · teams · accessFlow — automations
Visual pipelines for the routine: PR review, merge gates, follow-ups. A graph editor, human-approval nodes, and a git trail for every run.
DAG · wait-for-human · git trailPilot — a voice for Reef
Ask about your boards, sessions and settings, or say the command — "open a terminal for reef". Speaks and listens; risky actions still wait for your approve.
experimental · voice + textOne loop, four seats
Reef enters an organization through the manager, not around them — the planning and reading surfaces matter as much as the developer ones. Each seat gets the view it actually needs.
Every session under control from the desktop. Approvals in seconds, parallel agents that stay out of the way, the terminal one hotkey away.
An idea becomes an editable plan in minutes — worked out in a chat, checked against the board, ready to dispatch.
Live visibility into the team's AI activity: bottlenecks, waiting sessions, and cost per person and per task.
The operational picture in real time — burn-rate, forecasts, budgets and thresholds before the invoice arrives.
Reef is in private preview.
We are onboarding a small group of teams that build with AI agents every day. Tell us about yours.