How context engineering can save your organization from AI vibe code overload: classes from Qodo and Monday.com

As cloud venture monitoring software program monday.com’s engineering group scaled previous 500 builders, the workforce started to really feel the pressure of its personal success. Product strains have been multiplying, microservices proliferating, and code was flowing sooner than human reviewers may sustain. The corporate wanted a technique to overview hundreds of pull requests every month with out drowning builders in tedium — or letting high quality slip.

That’s when Man Regev, VP of R&D and head of the Development and monday Dev groups, began experimenting with a brand new AI instrument from Qodo, an Israeli startup targeted on developer brokers. What started as a light-weight check quickly turned a crucial a part of monday.com’s software program supply infrastructure, as a new case examine launched by each Qodo and monday.com in the present day reveals.

“Qodo doesn’t really feel like simply one other instrument—it’s like including a brand new developer to the workforce who really learns how we work," Regev informed VentureBeat in a current video name interview, including that it has "prevented over 800 points per thirty days from reaching manufacturing—a few of them may have induced severe safety vulnerabilities."

In contrast to code technology instruments like GitHub Copilot or Cursor, Qodo isn’t attempting to put in writing new code. As an alternative, it focuses on reviewing it — utilizing what it calls context engineering to know not simply what modified in a pull request, however why, the way it aligns with enterprise logic, and whether or not it follows inner finest practices.

"You possibly can name Claude Code or Cursor and in 5 minutes get 1,000 strains of code," stated Itamar Friedman, co-founder and CEO of Qodo, in the identical video name interview as with Regev. "You have got 40 minutes, and you’ll't overview that. So that you want Qodo to really overview it.”

For monday.com, this functionality wasn’t simply useful — it was transformative.

Code Overview, at Scale

At any given time, monday.com’s builders are delivery updates throughout tons of of repositories and companies. The engineering org works in tightly coordinated groups, every aligned with particular components of the product: advertising and marketing, CRM, dev instruments, inner platforms, and extra.

That’s the place Qodo got here in. The corporate’s platform makes use of AI not simply to test for apparent bugs or type violations, however to judge whether or not a pull request follows team-specific conventions, architectural tips, and historic patterns.

It does this by studying from your personal codebase — coaching on earlier PRs, feedback, merges, and even Slack threads to know how your workforce works.

"The feedback Qodo provides aren’t generic—they replicate our values, our libraries, even our requirements for issues like characteristic flags and privateness," Regev stated. "It’s context-aware in a approach conventional instruments aren’t."

What “Context Engineering” Truly Means

Qodo calls its secret sauce context engineering — a system-level strategy to managing every part the mannequin sees when making a choice.

This contains the PR code diff, after all, but additionally prior discussions, documentation, related recordsdata from the repo, even check outcomes and configuration knowledge.

The concept is that language fashions don’t actually “suppose” — they predict the following token based mostly on the inputs they’re given. So the standard of their output relies upon nearly completely on the standard and construction of their inputs.

As Dana Wonderful, Qodo’s group supervisor, put it in a weblog publish: “You’re not simply writing prompts; you’re designing structured enter beneath a set token restrict. Each token is a design resolution.”

This isn’t simply concept. In monday.com’s case, it meant Qodo may catch not solely the apparent bugs, however the refined ones that usually slip previous human reviewers — hardcoded variables, lacking fallbacks, or violations of cross-team structure conventions.

One instance stood out. In a current PR, Qodo flagged a line that inadvertently uncovered a staging atmosphere variable — one thing no human reviewer caught. Had it been merged, it might need induced issues in manufacturing.

"The hours we’d spend on fixing this safety leak and the authorized problem that it could deliver could be rather more than the hours that we cut back from a pull-request," stated Regev.

Integration into the Pipeline

Right this moment, Qodo is deeply built-in into monday.com’s improvement workflow, analyzing pull requests and surfacing context-aware suggestions based mostly on prior workforce code evaluations.

“It doesn’t really feel like simply one other instrument… It appears like one other teammate that joined the system — one who learns how we work," Regev famous.

Builders obtain recommendations in the course of the overview course of and stay in command of closing choices — a human-in-the-loop mannequin that was crucial for adoption.

As a result of Qodo built-in straight into GitHub by way of pull request actions and feedback, Monday.com’s infrastructure workforce didn’t face a steep studying curve.

“It’s only a GitHub motion,” stated Regev. “It creates a PR with the checks. It’s not like a separate instrument we needed to be taught.”

“The aim is to really assist the developer be taught the code, take possession, give suggestions to one another, and be taught from that and set up the requirements," added Friedman.

The Outcomes: Time Saved, Bugs Prevented

Since rolling out Qodo extra broadly, monday.com has seen measurable enhancements throughout a number of groups.

Inside evaluation reveals that builders save roughly an hour per pull request on common. Multiply that throughout hundreds of PRs per thirty days, and the financial savings rapidly attain hundreds of developer hours yearly.

These aren’t simply beauty points — many relate to enterprise logic, safety, or runtime stability. And since Qodo’s recommendations replicate monday.com’s precise conventions, builders usually tend to act on them.

The system’s accuracy is rooted in its data-first design. Qodo trains on every firm’s personal codebase and historic knowledge, adapting to completely different workforce types and practices. It doesn’t depend on one-size-fits-all guidelines or exterior datasets. All the things is tailor-made.

From Inside Software to Product Imaginative and prescient

Regev’s workforce was so impressed with Qodo’s affect that they’ve began planning deeper integrations between Qodo and Monday Dev, the developer-focused product line monday.com is constructing.

The imaginative and prescient is to create a workflow the place enterprise context — duties, tickets, buyer suggestions — flows straight into the code overview layer. That approach, reviewers can assess not simply whether or not the code “works,” however whether or not it solves the best drawback.

“Earlier than, we had linters, hazard guidelines, static evaluation… rule-based… you have to configure all the foundations," Regev stated. "But it surely doesn’t know what you don’t know… Qodo… feels prefer it’s studying from our engineers.”

This aligns intently with Qodo’s personal roadmap. The corporate doesn’t simply overview code. It’s constructing a full platform of developer brokers — together with Qodo Gen for context-aware code technology, Qodo Merge for automated PR evaluation, and Qodo Cowl, a regression-testing agent that makes use of runtime validation to make sure check protection.

All of that is powered by Qodo’s personal infrastructure, together with its new open-source embedding mannequin, Qodo-Embed-1-1.5B, which outperformed choices from OpenAI and Salesforce on code retrieval benchmarks.

What’s Subsequent?

Qodo is now providing its platform beneath a freemium mannequin — free for people, discounted for startups by way of Google Cloud’s Perks program, and enterprise-grade for firms that want SSO, air-gapped deployment, or superior controls.

The corporate is already working with groups at NVIDIA, Intuit, and different Fortune 500 firms. And because of a current partnership with Google Cloud, Qodo’s fashions can be found straight inside Vertex AI’s Mannequin Backyard, making it simpler to combine into enterprise pipelines.

"Context engines would be the huge story of 2026," Friedman stated. "Each enterprise might want to construct their very own second mind if they need AI that really understands and helps them."

As AI methods grow to be extra embedded in software program improvement, instruments like Qodo are exhibiting how the best context — delivered on the proper second — can remodel how groups construct, ship, and scale code throughout the enterprise.