Qodo’s Git‑integrated, human‑in‑the‑loop model eases AI code overload
Qodo just rolled out a tool that seems to answer a gripe many engineering squads have been muttering about: AI-spit-out code showing up in pull requests faster than anyone can give it a once-over. The startup slipped the service straight into the GitHub flow, popping up prompts the second a change is opened. So you don’t have to hop over to some weird dashboard or learn a brand-new UI - the help appears right where you already live.
It can throw out whole functions, but the design deliberately leaves the final say to a human. Monday.com’s infrastructure crew, for instance, managed to poke around with almost no learning curve because the integration rides on the same pull-request actions and comment threads they use every day. The tricky part, it turns out, is finding that sweet spot between automation and oversight - that balance probably made the difference between folks adopting it or ignoring it.
During a review, developers get suggestions but still hold the reins - a human-in-the-loop approach that felt essential for uptake. Since Qodo hooks directly into GitHub via PR actions and comments, Monday.com’s team didn’t have to wrestle with a steep onboarding process, which likely helped the tool catch on quickly.
Developers receive suggestions during the review process and remain in control of final decisions -- a human-in-the-loop model that was critical for adoption. Because Qodo integrated directly into GitHub via pull request actions and comments, Monday.com's infrastructure team didn't face a steep learning curve. It's not like a separate tool we had to learn." "The purpose is to actually help the developer learn the code, take ownership, give feedback to each other, and learn from that and establish the standards," added Friedman.
The Results: Time Saved, Bugs Prevented Since rolling out Qodo more broadly, monday.com has seen measurable improvements across multiple teams. Internal analysis shows that developers save roughly an hour per pull request on average.
Can a Git-integrated AI assistant actually keep up with a codebase that’s still growing? Monday.com’s engineers seem to think it can. Once their team topped five hundred developers, pull-request traffic jumped past what a handful of manual reviewers could handle.
That’s when Qodo stepped in, plugging its suggestion engine straight into GitHub pull-request actions and comment threads. Developers still get AI-generated tips, but the final call stays with a person - a human-in-the-loop set-up that, according to VP of R&D Guy Regev, proved key for getting people on board. The rollout didn’t need a lot of fresh training, so the infrastructure group avoided a steep learning curve.
Early feedback says the workflow shaved off some of the routine review time without obvious drops in quality. Still, it’s unclear whether the model will hold up as the number of microservices climbs and code velocity speeds up even more. The experiment shows that contextual AI, tightly tied to the tools teams already use and kept under human oversight, can ease some pressure; whether it scales more broadly will hinge on how well teams can keep the balance between automation and manual judgment.
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Common Questions Answered
How does Qodo's Git‑integrated tool surface AI suggestions during a pull request?
Qodo embeds its suggestion engine directly into GitHub pull‑request actions and comment threads, presenting prompts the moment a change is opened. This allows developers to see AI‑generated code recommendations without leaving the GitHub interface.
Why was the human‑in‑the‑loop model critical for adoption at Monday.com?
Monday.com's engineers needed to retain final decision authority over AI‑generated code, ensuring code quality and learning. The human‑in‑the‑loop design let reviewers approve, modify, or reject suggestions, which built trust and avoided a steep learning curve.
What problem does Qodo aim to solve for engineering teams with rapidly growing codebases?
Qodo addresses the overload of AI‑generated code flooding pull requests faster than manual reviewers can vet them. By integrating directly into the GitHub workflow, it streamlines review and prevents bottlenecks as developer headcount and PR traffic increase.
How does Qodo's integration differ from using a separate AI dashboard?
Unlike standalone dashboards, Qodo's tool appears within the existing GitHub UI, eliminating the need to switch contexts or learn new interfaces. This seamless integration reduces friction and keeps developers focused on the code they are already reviewing.
What role does the suggestion engine play in Qodo's workflow for Monday.com's infrastructure team?
The suggestion engine automatically generates function‑level code recommendations as part of pull‑request actions, helping the team handle the surge in PR volume. Engineers can then provide feedback, accept, or reject the suggestions, maintaining control while accelerating review cycles.