Fleet 1.13:Teams are now shipping 5x more PRs with autonomous pipelines.See what's new →
FleetFleet

Your engineers are already using AI agents.
Now manage them.

Fleet gives engineering leaders full visibility and governance over autonomous AI coding agents — across every repo, every team, every tool.

terminal
$ fleet init
✓ discovered .fleet/config.yaml
✓ registered 12 agents across 3 teams
$ fleet agent start --team backend
✓ started 4 agents
→ backend-dev-lead running
→ qa-engineer running
→ release-manager running
→ security-reviewer running
$ fleet status
fleet: 12 agents · 4 running · 0 blocked
pipelines: 2 active
last deploy: 2 min ago ✓
500+
Agents
120+
Templates
5 min
Setup

Works with

Claude CodeGitHubLinearJira

The shadow AI problem

Your engineers are already running agents.
You just can't see what they're doing.

Claude Code sessions are spinning up in every repo, writing code, opening PRs, sometimes merging them. No central log. No approval gates. No run-time limits. Every team invents its own duct-tape governance — or ships without any.

3am

Agent merge activity peaks outside working hours — nobody's watching

0%

Of teams have a unified audit trail for AI-generated code today

12hrs

Median time to detect a misbehaving agent once shipping starts

Without Fleet vs. With Fleet

From agent sprawl to a governed control plane

Most teams don't have a strategy for autonomous agents. They have a mess. Fleet turns it into infrastructure.

Without Fleet

  • Shadow AI in every repo — no inventory, no policy, no ceiling
  • PRs merged at 3am by agents with no reviewer and no audit trail
  • Every team reinventing its own approval process in Slack
  • No way to quarantine a rogue agent before it ships to prod
  • Agents run unbounded — no run-time limits, nobody knows what's burning hours
  • Compliance teams blocked from answering "who wrote this code?"

With Fleet

  • One control plane — every agent, every repo, every action logged
  • Approval gates enforced before merge, with human sign-off where required
  • Shared policies, run-time budgets, and agent roles defined once at the org level
  • A separate risk model that auto-quarantines agents at critical risk
  • Per-agent run-time budgets and run tracking, visible to leadership
  • Complete audit trail on every line: who proposed, who approved, who merged

What engineering leaders get with Fleet

Visibility, control, and governance for your AI-powered engineering 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. No more shadow AI in your codebase.

Agents That Coordinate Themselves

When a developer agent creates a PR, a reviewer agent picks it up automatically. No Slack pings, no manual assignment. Agents react to events across repos in real time.

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.

Enterprise-Grade Permissions

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

No containers. No cloud accounts. No DevOps overhead.

01

Configure

Define your agent teams, roles, and approval rules in a simple configuration file. 120+ templates mean you don't start from scratch.

02

Deploy

One command. No containers, no cloud accounts, no DevOps overhead. Fleet runs on your infrastructure in under 5 minutes.

03

Deliver

Agents react to events in real time. Code gets written, reviewed, approved, and merged — autonomously, with full audit trails.

See the config and CLI commands →
terminal
$ fleet init
Initialized .fleet/config.yaml with 6 agents
Created .fleet/prompts/ directory
Installed 5 skills to ~/.claude/skills/fleet/
$ fleet watcher start --supervised
Watcher started (PID 48291)
Label watcher: polling every 2m
Subscriptions: checking every 10s
Agent scheduler: cron active

What Fleet makes possible

Engineering teams using Fleet are shipping faster with fewer handoffs and full governance.

3-5x

Increase in weekly PR throughput when agents handle writing, review, and merging autonomously.

70%

Reduction in review bottleneck time. Dedicated AI reviewers pick up every PR in minutes, not hours.

$49/slot

Per parallel agent slot per month. A 10-agent team costs less than a single day of senior engineer salary.

Projections based on typical team deployments. Results vary by team size and workflow complexity.

Enterprise

Built for teams that need governance

Fleet's data plane runs on your infrastructure. 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.

Fleet by the Numbers

Built for reliability at scale

Installs in seconds. Zero infrastructure to maintain. Your fleet is production-ready from day one.

120+
Ready-Made Templates
4
Native Integrations
500+
Concurrent Agents
5 min
Setup, Zero Infrastructure

See Fleet in action

Book a 15-minute demo and see how Fleet gives you visibility and control over your AI-powered engineering team.