AI workflows for the work you repeat
Stop re-prompting.
Start shipping.
The prompt you rebuild every week — same instructions, same pasted context — becomes a saved Fleet workflow. The context pulls straight from your own files, an independent review checks the output, you sign off, and it ships. On a schedule, on your infrastructure.
Works with
Now build any agent workflow on a canvas.
Drag typed steps together — draft, review, approve, publish — and gate the run on a human sign-off. Bounded retries and a full audit trail on every run. Self-hosted, on Claude Code.
The chart is not the decision. Fleet closes the loop.
Map product or operational evidence to an outcome, see what needs attention now, and start the governed workflow that should change it. The signal, verdict, and response stay together.
Map a signal you trust
Use a numeric metric published by a connected integration, or begin with a manual observation.
Work the ordered queue
Evidence problems stay separate from breached and at-risk outcomes, while healthy KPIs stay collapsed.
Start the response in context
Launch the bound saved workflow from current evidence, or create one with the KPI context attached.
The re-prompting problem
You rebuild the same prompt every week.
The output still needs checking.
Same instructions, same pasted context, every time — and the chat window saves neither. When the draft lands, someone still has to fact-check it, route it, and decide it's safe to ship. Fleet saves the prompt as a workflow, pulls context from your files, runs an independent review, and waits for your sign-off.
The same prompt, rebuilt from scratch in a chat window that saved nothing
The real work of a recurring task: re-pasting context the chatbot forgot
Record of who reviewed the output and who approved what shipped
Without Fleet vs. With Fleet
From re-prompting to a saved workflow that ships
The work already runs through AI. The difference is whether it runs through a chat window or through a workflow you can trust.
Without Fleet
- The same prompt rebuilt in a chat window, every single week
- Context re-pasted by hand — and stale the moment the source files change
- Output checked by whoever has time, or not checked at all
- Approval happens in a Slack thread nobody can find later
- Agents run unbounded — no run-time limits, nobody knows what's burning hours
- No record of what was asked, what shipped, or who said yes
With Fleet
- The prompt saved once as a workflow — rerun it, don't rewrite it
- Context pulled fresh from your own files on every run
- An independent review step checks the output before anyone signs
- Approval gates with human sign-off, recorded with name and timestamp
- Per-agent run-time budgets and run tracking across the whole fleet
- Complete audit trail on every run: who proposed, who approved, what shipped
What you get with Fleet
Visibility into every agent, governed workflows, budgets, and permissions that match your team.
Autonomous Release Pipelines
Define multi-stage workflows that move code from development through review to production — with human approval gates where you need them. No manual handoffs.
Full Visibility Into Every Agent
See what every AI agent is doing, right now. Start, stop, and manage any agent instantly — nothing running that you can't see.
Agents That Coordinate Themselves
A saved workflow carries work from development into review, approval, and merge. Labels and schedules start explicit definitions; the graph owns every handoff.
Risk Detection Before Damage
Fleet evaluates every agent across 6 dimensions, and a separate risk model auto-quarantines any agent that hits critical risk — before it becomes an incident.
Works With Your Existing Stack
Fleet runs your agents on Claude Code and plugs into GitHub, Linear, Jira, and MCP. Your developers keep their workflow — you get the governance layer on top.
Permissions That Match Your Team
Mirror your org structure — CEO to intern. Control who sees what, who approves what, and the run-time budgets each team's agents operate under.
From setup to shipping in 5 minutes
One Go binary, no Docker stack, and self-hosted workflow execution.
Configure
Define your agent teams, roles, and approval rules in a simple configuration file. 120+ templates mean you don't start from scratch.
Deploy
Install one Go binary with no Docker stack. Connect it to the hosted dashboard, then run workflow steps on your own infrastructure.
Deliver
Labels, schedules, or a person start saved workflows. Code moves through explicit review, approval, and merge steps with a full run history.
See the config and CLI commands →
What Fleet makes possible
Execution, review, approval, and run history stay together in one governed workflow.
Steps, retries, artifacts, approvals, and terminal status stay attached to each workflow run.
Flagged work returns through an explicit fix loop, with a defined limit and a human gate available before merge.
Per fleet, per month — unlimited roles and an included agent-run throughput allowance.
Built for teams that need governance
AI agent fleet management that runs on your infrastructure — the data plane stays with you; the dashboard and analytics live at app.fleetctl.ai. Fleet meets your security and compliance requirements out of the box.
Data sovereignty
Run the model on Bedrock or Vertex in your own cloud. Your source code stays private.
Audit trail
Every agent decision logged — your compliance team will thank you
Run tracking
See cumulative run time, total runs, and last activity for every agent
Role-based access
Mirror your org chart. Department heads see their teams; developers see their repos.
Built for reliability at scale
Installs in seconds. No container stack to maintain. Your self-hosted worker stays under your control.
See Fleet in action
Start free and turn the prompt you rebuild every week into a saved workflow — reviewed, signed off, shipped. Or book a 15-minute demo for a walkthrough.