Shared mission context
Every agent sees goals, constraints, artifacts, approvals, and source links before it acts.
AAtlasResearch
RunningFForgeBuilder
ThinkingVValeReviewer
ReviewingNNoraOperator
DonePlanning release lane and assigning specialist agents.Research agent found 7 relevant repo conventions.Builder agent opened a production preview artifact.Reviewer agent approved the deployment checklist.
Multi-agent operations
Build with agent teams that move like one product crew.
Agent Teams coordinates specialist AI workers across research, code, review, deploys, and reporting with visible state, shared memory, and human approval gates.
18.4k
Tasks routed
+31%
42s
Avg handoff
-58%
96%
Review pass
+12%
Platform
A command center for autonomous work.
Parallel execution
Run specialist agents at the same time while the coordinator keeps state and sequencing clean.
Human approval gates
High-impact actions pause for review with plain summaries, diffs, and rollback context.
Workflow
From prompt to production with visible handoffs.
A coordinator agent keeps the team honest: it routes work, checks readiness, and records what changed.
Integrations
Give agents the same tools your team already uses.
Connect code, tickets, chat, docs, deployment surfaces, and browser automation into one supervised execution graph.
GitHub
Linear
Slack
Vercel
Notion
Browser
Terminal
OpenAI
Launch
Start with one agent team, then scale into a full operating layer.
Use Agent Teams for product launches, repo maintenance, internal operations, support triage, and deployment workflows.
Early access
$49
per operator workspace
- Agent coordinator and 6 specialist slots
- Workflow memory and artifact history
- Approval gates for external actions
- Deployment and review summaries