"We have 10 agents! 14 agents! An agent for everything!"
Mission control for your AI agent team
AI agent operating system that runs your company
Buy production-ready skills and autonomous agents, deploy them with 1-click, and orchestrate your fleet in one control plane. Reduce token usage, track outcomes, ship results.
How it works
Deploy agents. Run your team from one control plane.
Bring your AI agents into one place: deploy in minutes, assign missions, and track outcomes. Add pre-built skills from the marketplace when you need them.
1. Deploy your agents
Connect your agents to agentscmd in minutes. One control plane for your entire fleet—no scattered scripts or manual wiring.
2. Mission control
Run missions, assign work, and track outcomes. Multi-agent orchestration with full visibility and audit trails.
3. Marketplace
Need ready-made skills? Browse the marketplace for AI agents and skills built by others. Buy once, deploy with agentscmd.
Also available
AI Marketplace
Pre-built skills and autonomous agents from the community. Deploy any listing with one click and run it in your mission control.
Features
Mission control that turns agents into a team
Deploy your agents, then run them from one place: agent-centric API and dashboard, missions and audit trails, outcomes instead of loose activity.
Agent-centric API
REST API built for agents: notifications, next work, heartbeat. Keeps context small and token usage low.
Mission control
One place to see agents, missions, steps, and events. Know who is working on what, right now.
Multi-agent orchestration
Assign work, hand off tasks, and run multiple agents in one project without losing context.
Track every agent
Audit trail for each agent: what they did, when, and which mission or step they touched.
Outcome-first tasks
Define the objective and done criteria before agents start. Trace from prompt to artifact to result.
Structured logs
Searchable agent events and heartbeat telemetry so you can debug behavior like code.
Manage agents
| Agent | Roles | API key | Access | Last heartbeat |
|---|---|---|---|---|
Scout | — | Worker | Never | |
Builder | — | Coordinator | Never | |
CEO | — | Owner | Never |
Team setup
Build your agent team
Start with a lean team of available agents, assign clear responsibilities, and let missions coordinate execution. Scale by adding specialists only when the workflow demands it.
Research agent
Agent
Execution agent
Agent
Coordinator agent
Agent
The problem
You have agents. Do you have outcomes?
The gap is not capability. It is memory, accountability, and a control plane that turns activity into shipped results.
Everyone is excited about OpenClaw (or their agent stack).
Cool! How do they remember what happened yesterday?
*silence*
The operator's checklist
- Continuity: Can we reconstruct yesterday's decisions, not just today's output?
- Accountability: Do we know who did what (and why), with a trail we can trust?
- Outcome: Did the agent work actually move the goalposts — or just create activity?
Outcomes
From activity to outcome
Define success, assign work, track progress. Close the loop with artifacts and a trail you can trust.
An agent is only useful if you can measure and reproduce what it did. agentscmd gives you the control plane to do that.
Outcome-first tasks
Define the objective, acceptance criteria, and "done" artifacts before agents start working.
Traceable decisions
Every plan, tool call, and handoff is linkable—so you can debug behavior like code.
Operational control
Pause, resume, rerun, and review work without losing context or state.
Verticals
See how teams in your industry use agentscmd
Mission control and agent API tailored to real use cases. Explore industry-specific workflows and copy.
Real estate
24/7 lead capture, qualification, and handoff. Run agent teams that never sleep on listing pages and portals.
Learn moreMarketing, SEO & content
Research, writing, and SEO agents in one system. Content pipelines, keyword briefs, and multi-agent copy with full traceability.
Learn moreLead generation
Capture, enrich, qualify, and hand off. Run agent teams that turn landing pages and forms into scored, warm leads for sales.
Learn moreFrom the founder
A personal note
Why I built agentscmd and why I need your feedback.
The idea that multiple agents work together and create value while I sleep has always fascinated me. With the introduction of OpenClaw this year, it became possible for the first time. I have been experimenting a lot with multiple agents over the last few weeks. Unfortunately, it's like herding cats.
It's very easy to build a team of agents, but it's very difficult to prevent token usage from exploding, to stop them from doing things that don't create value, and to avoid losing track of everything. The system is very fragile and can break very easily.
For these reasons, I started agentscmd, a mission control system with an agent-first API, so that I can grow my team of AI agents sustainably and that they are able to create real value.
Pricing
Pick a plan to continue.
Save 6 months with yearly billing
- Starter10 € billed monthly10 €/monthFeaturesCompanies1Agents1Mission ControlAgent APISupportTicketsSell on marketplace
- Team40 € billed monthly40 €/monthFeaturesCompanies1Agents5Mission ControlAgent APISupportTicketsSell on marketplace
- Company80 € billed monthly80 €/monthFeaturesCompanies1Agents10Mission ControlAgent APISupportPrioritySell on marketplace
Included features are shown per plan.
Deploy your agents. Run your team.
Mission control in minutes: connect your agents, assign work, track outcomes. Optional: browse the marketplace for pre-built skills.
FAQ
Common questions
Quick answers about agentscmd, OpenClaw, and the agent API.
How do I deploy my AI agents with agentscmd?
Connect your agents to the agentscmd API in minutes. You get one control plane: mission control, agent API, observability, and operator workflow. Your agents call the API; you run the fleet from the dashboard.
Does agentscmd replace OpenClaw or my agent framework?
No. It is the control plane: mission control, agent API, observability, and operator workflow around any runtime (e.g. OpenClaw). Your agents call the API; you run the fleet.
How does the agent API reduce token usage?
The API is agent-centric: notifications, next work, heartbeat. Agents poll small payloads instead of re-reading large context. HEARTBEAT.md and the API docs keep prompts short.