What is FleetQ?
FleetQ is an AI agent mission control platform. You define goals; FleetQ deploys agents, monitors them, enforces budgets, and delivers results — automatically.
Whether you're automating competitive research, lead qualification, content generation, or customer outreach — FleetQ gives you a production-grade operating layer for AI agents without writing infrastructure code.
What you can do with FleetQ
Run AI Pipelines
A 20-state experiment engine orchestrates multi-step AI workflows with automatic checkpointing, retries, and approval gates.
Coordinate Agent Teams
Crews let multiple agents collaborate — a coordinator assigns tasks, specialists execute them, reviewers validate the output.
Schedule Recurring Work
Projects run AI workflows on a schedule — hourly, daily, weekly, or cron. Every run is tracked, budgeted, and audited.
Key concepts
| Concept | What it is |
|---|---|
| Experiment | A single AI pipeline run — scored, planned, built, executed, and evaluated through up to 20 states. |
| Agent | An AI worker with a role, goal, and backstory. Agents power every experiment, crew task, and workflow node. |
| Skill | A reusable, versioned capability — an LLM prompt, connector call, rule engine, or guardrail. |
| Crew | A team of agents working in parallel or sequence, coordinated by an orchestrator. |
| Workflow | A visual DAG (directed acyclic graph) of nodes: agents, conditions, human tasks, loops, and more. |
| Signal | Any inbound event — a webhook payload, RSS item, CRM lead, or manual entry. |
| Project | A container for scheduled, recurring AI work. Each trigger creates a ProjectRun with full history. |
| Approval | A human-in-the-loop gate. Agents pause for human review before taking irreversible actions. |
| Tool | An external capability (MCP server, shell, browser) that extends what agents can do beyond language generation. |
Ready to see it in action?
Follow the quick start guide and run your first AI workflow in 5 minutes.