Context
As an in-house Product Designer at Budibase, I led the end-to-end UX redesign of the core workflow builder, the visual automation engine at the heart of the platform. Budibase is an open-source, low-code platform that enables businesses to rapidly build custom applications like customer portals, admin panels, and internal tools. The work had to support a wider strategic pivot: positioning Budibase as an AI-first automations platform, not only a better builder for today’s users.
The problem
The builder worked, but you could not see your data. People could not tell what data was flowing into or out of a step, or even what they had selected in the tab they were in. Debugging was guesswork, errors did not explain themselves, and the complaints kept landing in the community Discord: about eight to ten at a time on automations alone. It also left the product unprepared for the planned shift to AI-first automations, which would depend on every step showing its data clearly.
What the research said
I designed and ran a paid Maze study with 25 active users. The task was blunt: go through the product and name the most frustrating part. The pattern was clear. Data visibility and unclear errors came up again and again, along with a need for better debugging and testing.
I put the findings into a PDF and presented it to the Head of Engineering, the CMO, and the CEO. That evidence set the roadmap for the redesign, and for the longer-term shift toward AI-first automations. The answer was not one-off UI fixes. It was a data framework: every step shows what goes in and what comes out.
What shipped
| Improvement | Change | Benefit |
|---|---|---|
| Data In / Data Out | I added inline tabs to show the data flowing into and out of each step of a workflow. | Complete transparency for tracing data. |
| Inline Error Logging | I added a dedicated tab with clear error messages for any failed step. | Less guesswork, faster troubleshooting. |
| New Sidebar Panel | I moved workflow controls out of the main canvas and into a dedicated, contextual sidebar. | A cleaner canvas and a clearer editing experience. |
| Enhanced Canvas & UI | I added zoom and pan, and made the layout more compact. | Easier to manage large, complex workflows. |
What happened
The complaints stopped. Before launch, automations drew maybe eight to ten complaints and feedback requests on the Budibase Discord. After I shipped the redesign it was one, I believe, or none. Clearer errors meant fewer people needed help in the first place.
What it led to
Budibase later added an agents section, and it was built on top of automations. Agents work because every step now shows and confirms the data coming in and out, so an agent can read that data and understand the context of what is happening. A new email arrives, an agent classifies it, creates a Jira ticket, and assigns it to the right team. The data in and data out framework is what made that possible, and it is part of how Budibase now competes with AI workflow tools like n8n.
At the time I worked with ChatGPT and the AI tools that existed then.
Conclusion
Twenty-five users told me where it hurt, I presented that to the executive team, and I redesigned the builder so people could finally see their data. The same structure went on to carry the agents feature, so the AI platform Budibase became was built on workflows users already understood, not started from scratch.
