The autonomous code layer

Ship code.
Write docs.
Report back.

An AI agent that watches your backlog, writes the code, runs the tests, ships the PR, and sends you a progress update — all without a human in the loop.

deployos agent --watch
00:00:04 READY Assigned to sprint #47
00:00:08 PROGRESS auth/middleware.ts — writing token validation
00:00:31 PROGRESS auth/routes.ts — adding OAuth2 endpoints
00:01:04 DONE tests/auth.test.ts — 23/23 passing
00:01:22 SHIPPED PR #891 opened — 4 files, 312 lines
00:01:23 DOCS api-auth.md updated
00:01:23 Slack sent to #eng: PR #891 ready for review
Terminal showing autonomous code deployment

How it works

One goal in. Everything out.

Connect your repo, give the agent a task, and watch it run the full development loop — code, tests, docs, deploy.

01

Assign

Drop a task from Linear, GitHub Issues, or plain English. The agent parses it, plans the implementation, and starts working.

02

Code

Agent reads the codebase, writes the implementation across files, runs tests on every change, and fixes its own errors in real time.

03

Ship

Opens a PR with clear descriptions, updates the task status, notifies the team — all while you focus on the next thing.

Code pipeline visualization

The agent surface

It handles the loop. You handle the product.

DeployOS isn't another chat window. It's an agent that lives in your infrastructure, has context on your codebase, and works through your CI/CD pipeline like a senior developer who never sleeps.

Autonomous PRs

Writes, tests, and opens pull requests without stopping for approval. Configure the autonomy level per task type.

Doc generation

Auto-updates READMEs, API docs, and changelogs as code changes. Never ship code with stale docs again.

Live progress

Streaming status updates to Slack, Linear, or your own webhook. Real-time visibility without watching a terminal.

Self-healing

Tests fail, agent reads the error, rewrites the fix, runs again. Iterates until it passes or hands off to you for a decision.

Principles

1

Progressive autonomy

Agents start narrow. Scope expands only after the pipeline earns trust at each level. No production deploys until the harness proves reliable.

2

Observability by default

Every agent action is logged. Every failure surfaces to the right person. The system doesn't hide what it's doing.

3

Spec-driven, not prompt-driven

Vague prompts produce unreliable output. DeployOS agents work from structured specs so intent is explicit and errors are caught early.

In active development

The codebase ships itself.
Your job is to decide what to build.

DeployOS is a developer-native platform for teams who want AI to handle the entire implementation loop — from spec to deployed PR — while humans focus on direction.

Early access for teams with active CI/CD infrastructure.