Vision

AI DevKit exists to turn scattered AI coding agents into one local system.

It inherits the idea from The New Engineering Workflow in AI changes the Tools, you still own the Craft: empowering engineers to integrate AI effectively into a structured, test-driven, and craftsmanship-focused development process.

We believe AI coding agents are most powerful when developers can operate them deliberately: one setup, one local view of running sessions, reusable memory, clear communication paths, and disciplined engineering workflows when the task is risky.

AI DevKit provides the control plane for that structure. It helps engineers reduce repeated prompting, keep context in sync, share memory across sessions, route work between agents, verify work before completion claims, and make the same operating model available across coding agents.

Our goal is simple: To make operating multiple AI coding agents feel coherent: the agents you choose, running through one local system that knows your project, your style, and your standards.

A toolkit that enables developers to:

• Operate many agents locally - See running sessions, send work, and avoid scattered terminal tabs
• Configure once - Generate agent setup from one project-local source of truth
• Plan before code - Capture requirements, design decisions, and implementation tasks before agents edit files
• Remember project context - Reuse decisions, conventions, and fixes across sessions
• Verify completion - Require fresh evidence before work is marked done
• Review before push - Make code review part of the agent workflow, not an afterthought
• Keep agents consistent - Use one operating model so every supported coding agent follows the same setup, memory, and verification rules

1. Plan before code - Start with clear requirements and design before implementation
2. Evidence before done - Treat test/build output as part of completion, not an optional follow-up
3. Durable context - Keep project knowledge in docs and memory so agents do not start from zero
4. One operating layer - Make setup, memory, communication, and workflow travel across the coding tools your team uses
5. Low friction - Make disciplined multi-agent work easier than repeating prompts manually

We envision a future where developers use many specialized coding agents without managing them through scattered terminals, duplicated setup files, and forgotten context. The goal is not full autonomy; it is a disciplined multi-agent development loop where humans keep ownership and agents share the same setup, memory, communication paths, and engineering standards every time.

AI DevKit is our contribution to that future.