An AI Software Development Workflow That Stays in Control

What a managed AI software development workflow looks like

Most AI coding sessions are a single chat window: you ask, the model answers, and you hope the diff is correct. That works for a snippet, but it falls apart across a real project with dozens of tickets, branches, and review steps. An AI software development workflow needs structure — a place to plan the work, route it to the right tool, capture the output, and decide what actually ships.

AutoPilot is an AI Dev Control Center: a local-first desktop app that wraps the agents you already use in a real delivery process. It does not replace your coding models. Instead, it manages the workflow around them so that every change moves through planning, implementation, review, testing, and approval — with you in control at each gate. You can see how the pieces fit together on the AutoPilot homepage or jump straight to the AI Dev Control Center overview.

Claude develops, Codex reviews, AutoPilot manages

The workflow is built on a clear division of labor. Each role does what it is best at, and AutoPilot keeps them coordinated.

• Claude develops. It writes and implements code against a ticket’s scope. See the Claude development workflow.
• Codex reviews. It validates the change, flags regressions, and proposes corrections before anything is merged. See Codex code review.
• AutoPilot manages. It owns the projects, tickets, approvals, execution, logs, and memory that tie the two together.
• You stay in control. Nothing executes or ships without your explicit approval.

This separation is what turns ad-hoc prompting into a repeatable process. Implementation and review are handled by different agents, so a single model is never grading its own homework, and you have a clear record of who proposed what.

Plan, execute, test, and approve — every step logged

A workflow is only trustworthy if you can audit it. AutoPilot records each action as it happens, so you can trace a shipped change back through its review, its implementation, and the ticket that started it. Persistent memory carries project context forward between sessions, so the agents are not starting cold every time.

You decide the cadence: review each step manually, or let a sequence run and inspect the logs afterward. Either way, approvals are explicit and reversible. Teams that want a structured backlog can lean on AutoPilot’s AI project management for developers to keep tickets, priorities, and status in one place.

Local-first, free to start

AutoPilot runs on your machine. Your code, your repositories, and your project history stay local — they are not uploaded to a third-party service to make the workflow work. The desktop app is a free download, with a Pro tier for teams that need more. Browse the getting-started guide to set up your first project, or download AutoPilot free and watch the product demo to see the full loop in action.