AI coding agents: JetBrains workflow infrastructure analysis

The coding agent has quietly moved from shiny autocomplete goblin to office plumbing. Less glamorous, yes, but much more likely to flood your sprint if nobody owns the pipes. JetBrains Research has put fresh survey scaffolding around that shift with AI Coding Agents: Adoption Trends, based on its Developer Ecosystem Survey 2026. The important part is not that developers are trying agents. It is that teams now need to decide whether these tools are personal productivity snacks or shared engineering infrastructure.

What happened: JetBrains puts a number on agent creep

JetBrains Research says AI Coding Agents: Adoption Trends is based on the Developer Ecosystem Survey 2026, the tenth edition of its large scale, globally representative study run by the Strategic Research and Market Intelligence team. The company says the survey covers more than 15,000 professional developers worldwide, which is a better sample than whoever is yelling in your team chat after installing three extensions before lunch. JetBrains also says this post follows an April 2026 report on adoption of major AI coding tools. In other words, the measurement has moved from curiosity to category tracking. The headline number is the one every engineering leader will be tempted to screenshot: according to JetBrains Research, as of May to July 2026, 90% of professional developers were using AI coding agents at work. That is not a niche feature hiding under an experimental flag. It is a workflow reality, which means ignoring it is now also a decision. The question becomes less should developers use agents and more where agents are allowed to act, what they can touch, and how their output gets reviewed.

Why it matters: JetBrains is packaging plumbing, not confetti

JetBrains AI describes its ecosystem as AI for professional software development, including in IDE assistance, agent driven workflows, and governance for teams. That framing matters because a coding agent is no longer just a text box with ambition. JetBrains lists JetBrains IDEs, a choice of AI agents, governance and control, runtime and orchestration, and evaluation and optimization as parts of its AI ecosystem. That is infrastructure language, which is what happens when autocomplete grows up and starts attending architecture review meetings. JetBrains AI also emphasizes freedom to choose coding agents without vendor lock in, plus enterprise ready privacy and controls with centralized visibility, governance, security controls, and deployment flexibility. Strip out the brochure gloss and the useful signal is clear: teams want choice, but leadership wants observability. A dozen developers quietly using different agents is experimentation. A company standardizing on agents without shared controls is just copy paste with a nicer onboarding screen.

What to measure: JetBrains data makes adoption a baseline, not a victory lap

JetBrains Research provides adoption evidence, but adoption alone does not answer whether an agent improves engineering outcomes. A team should measure accepted agent changes, review rework, test failures, security findings, and defect patterns after merge before naming a default tool. That sounds less thrilling than promising everyone a robot pair programmer, but production has historically preferred boring paperwork over vibes. Production is rude like that. The practical move is to evaluate agents in the places where work actually bottlenecks. If an agent speeds up scaffolding but increases review time, the team did not save time. If it drafts tests that developers keep rewriting, measure the rewrite, not the demo. If it helps senior engineers move faster but confuses juniors into approving plausible nonsense, the rollout needs guardrails, training, or a smaller blast radius.

What comes next: JetBrains has moved the question up the org chart

JetBrains AI points toward centralized visibility, governance, security controls, and deployment flexibility, which are exactly the areas teams should pressure test before standardizing. Ask who can enable agents, which repositories they can access, what context they receive, and how generated changes are reviewed. Ask whether teams can compare agent output across projects without turning developer workflows into surveillance theater. Yes, that balance is annoying. So is Kubernetes, and we still let it into the building. For readers building or buying AI development tooling, the takeaway is simple: treat coding agents like workflow infrastructure before they become accidental infrastructure. JetBrains Research suggests the user base is already there, so the next advantage comes from measurement, governance, and honest feedback loops. Watch for vendors to compete less on having an agent and more on proving where that agent helps without laundering risk through developer enthusiasm. The agent era is not arriving with trumpets. It is arriving as a checkbox in your IDE settings, which is somehow more ominous and more useful.