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AI coding agents can generate pull requests at a pace your team has never seen. The bottleneck has shifted from writing code to everything that follows: reviewing, iterating, and merging. AI code-to-merge platforms are the category of tools built to manage that entire lifecycle, from the moment an agent starts working to the moment code lands in your main branch. This article walks through ten questions you should ask before committing to a platform.

We put a blunt question to developers on a recent live webinar: right now, could you actually prove AI agents are paying off for you or your team? Only 24% said yes. The other 76% were guessing, unsure, or already suspicious that agents are costing more than they’re saving. That gap between adoption and proof is the real story in agentic development right now. Teams aren’t behind on running agents. They’re behind on knowing whether it’s working.

The recent mystery around Ox Alpha last week and the week before was a fun slice of what makes social media fun. The hunt for the model provider and how people did that discovery should be studied. But, this post is more about stealth models in general. You might be wondering what the phrase “stealth model” even means and if you already know, you might still be curious about why companies release stealth models.

Giordano “Gyo” Piazza, GitKraken’s Director of Engineering, has spent the past month heads down on the next version of Kepler. When he got on a call to talk through it, he skipped the changelog and went straight to the part he actually wanted to talk about: how agents show their work.

Perplexity was a big name a few years ago. But, we haven’t heard much out of them lately. There are plenty of posts on social media criticizing Perplexity to that effect. One thing that the posts miss is that Perplexity is still the king of AI search. Google is giving it a run for its money with the AI previews on Google search, but Perplexity still wins out in several measurable ways.

Choosing a software engineering intelligence platform is one of those decisions that gets more complicated the longer you wait. The category has grown rapidly, and every vendor claims to track the metrics that matter. GitKraken Insights gives you a full-stack view of engineering performance, from DORA metrics to AI tool ROI, in a platform trusted by over 40 million developers. But it isn’t the only option on the market.

Your code review tool does more than display diffs. It shapes how your team shares knowledge, catches bugs, and ships software. With AI-generated code accelerating PR volume, choosing the right code review platform is a decision that directly affects your team’s velocity. GitKraken connects PR workflows, multi-repo management, and AI-assisted review into a single developer experience platform.

We hosted a live launch preview, “Level Up Your Agentic Workflow,” to introduce two things at once: GitKraken Insights for Developers and Kepler. Jeremy Castile, our VP of Developer Research, opened with research on how teams are actually using AI agents today. If you want the full breakdown of that data, including the adoption numbers and the gap between feeling faster and proving it, we published it separately in State of AI in Engineering 2026: The Proof Gap.

The biggest Kepler update since public preview is live. If you tried Kepler early, here’s what changed, and the problem it’s built to solve.

If you look at the people posting to social media the most about AI, you’ll probably feel left behind. They’re running dozens or hundreds of agents and probably shipping more than you. But, they are not representative of the rest of the dev world. They are the 1% of the 1%. The thing that might be missed is that you reading their posts is probably separating you from a much larger cohort of devs that are still trying to figure out how to use AI effectively in their day to day work.