Anyone can generate the code. Shipping it predictably is the hard part.
One shared space for your team and your AI agents. GitHub holds your code, DevStride holds the plan, and delivery runs on a loop: AI proposes, you approve, it applies. You keep the judgment.
The plan is the system. AI does the loop.
Your team adopted AI and got faster. The bottleneck just moved to planning, QA, and keeping it all coordinated. Here's how DevStride closes that gap, with your judgment in charge.
Delivery runs on a loop
AI reads the plan and works it a step at a time, writing code, docs, and progress back into DevStride. It stops at each real decision instead of running ahead of you.
You keep the judgment
AI proposes, you approve, it applies. Nothing ships without your sign-off. The loop we run to build DevStride itself is documented in the open.
See how we build →One system of record
From intent to shipped, every item tied to its plan step and its pull request. Someone with zero context can see what happened, why, and in what order.
Your agents get real tools, not a chat box.
Your people work in the app. Your agents work through the DevStride MCP, in plain language, against the same live plan. Connect from the Connect AI page in the app and authorize over OAuth 2.1.
search_items
get_item_hierarchy
bulk_update_items
create_roadmap
get_throughput
Works with Claude, ChatGPT, Cursor, Copilot, Codex, and Gemini CLI.
Open data model underneath: your agents act through the MCP, your BI reads a live replica, your engineers can see the source.
Explore the MCP Server →Our own Service Desk was planned with Claude, through the MCP.
Phil planned DevStride's Service Desk with Claude through the DevStride MCP. The loop executed it a step at a time, every item tied to its pull request, and DevStride stayed the system of record throughout.
No separate status doc, no reconstructing the history after the fact. The plan carried the intent, the loop did the work, and the trail stayed intact.