Hopper is the first agentic development environment for the mainframe. It's Cursor for mainframes. It combines a real TN3270 terminal, mainframe-aware panels for datasets, jobs, members, and spool output, and an AI agent that can operate across z/OS workflows. The agent can inspect datasets, read and edit PDS members, write JCL, submit jobs, parse JES output, explain failures, and help developers debug mainframe workflows faster. Hopper is available on Windows, Linux, and macOS.
Today we’re launching Hopper, the first agentic development environment for mainframes.
Mainframes running on COBOL are the 60-year-old computing platforms that still quietly run much of the modern economy: banks, payments, insurance, airlines, government systems, and more.
But they were built for expert humans using terminal screens, function keys, batch jobs, datasets, and highly specific workflows, not AI agents.
Modern AI coding tools assume GitHub, shells, files, package managers, and test runners. Mainframes are a completely different computing paradigm.
Hopper combines a real mainframe terminal, context panels for datasets and jobs, and an AI agent that can safely operate across mainframe workflows.
Our goal is simple: bring AI agents to the legacy systems that still run the world without pretending they are modern codebases.
You can request access to a mainframe on our page, and start playing with Hopper to see what an agentic mainframe environment feels like.
I'm studying AI and we barely even touch COBOL. Does Hopper have any way to help the agent understand undocumented business logic, or does it only work with what's explicitly in the code and job outputs? Feels like that gap could make or break it for real enterprise adoption
Agentic AI is hitting the legacy systems. Bridging agentic AI to z/OS is genuinely hard—mainframes don't have Git/files/shells.
Curious to know how you are managing the AI agent’s memory and context while debugging. On mainframes, logs and job outputs can become huge, and the agent also has to remember the sequence of ISPF screens, multiple PDS members, and earlier debugging steps.
Genuinely didn't expect agentic AI to hit mainframes in 2026. The hard part isn't just terminal access — COBOL business logic is often undocumented and lives in people's heads. How does Hopper handle cases where the agent needs context that's nowhere in the codebase?
About Hopper on Product Hunt
“First agentic development environment for mainframe/COBOL”
Hopper launched on Product Hunt on May 12th, 2026 and earned 93 upvotes and 10 comments, placing #17 on the daily leaderboard. Hopper is the first agentic development environment for the mainframe. It's Cursor for mainframes. It combines a real TN3270 terminal, mainframe-aware panels for datasets, jobs, members, and spool output, and an AI agent that can operate across z/OS workflows. The agent can inspect datasets, read and edit PDS members, write JCL, submit jobs, parse JES output, explain failures, and help developers debug mainframe workflows faster. Hopper is available on Windows, Linux, and macOS.
Hopper was featured in Productivity (651.7k followers), Developer Tools (512.4k followers) and Artificial Intelligence (468.5k followers) on Product Hunt. Together, these topics include over 294.5k products, making this a competitive space to launch in.
Who hunted Hopper?
Hopper was hunted by Chris Messina. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Hopper stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Chris Messina
Hey Product Hunt 👋
Today we’re launching Hopper, the first agentic development environment for mainframes.
Mainframes running on COBOL are the 60-year-old computing platforms that still quietly run much of the modern economy: banks, payments, insurance, airlines, government systems, and more.
But they were built for expert humans using terminal screens, function keys, batch jobs, datasets, and highly specific workflows, not AI agents.
Modern AI coding tools assume GitHub, shells, files, package managers, and test runners. Mainframes are a completely different computing paradigm.
Hopper combines a real mainframe terminal, context panels for datasets and jobs, and an AI agent that can safely operate across mainframe workflows.
Our goal is simple: bring AI agents to the legacy systems that still run the world without pretending they are modern codebases.
You can request access to a mainframe on our page, and start playing with Hopper to see what an agentic mainframe environment feels like.
Would love to hear any feedback or comments!