From Data Discovery to AI-assisted Action
Designed Select Star's first AI agent — an auto-tagging system that moved users from finding data to acting on it. The agent processed 22K+ queries and governed 12K+ assets, delivering 6× engagement over legacy catalog workflows.
processed
through auto-tagging
legacy catalog workflows
Context & Challenge
The shift: from understanding data to acting on it
Select Star had built a strong foundation in data discovery — search, catalog, and lineage helped teams understand their data. But understanding wasn't enough. Customers like Amplitude were asking for tagging to "just work" automatically. The real gap was the last mile: moving from insight to action.
With no dedicated PM and a team already stretched across the context layer, AI settings, and LLM search, I had to drive alignment quickly. I wrote a lightweight PRD and mockups to get the CEO and engineering aligned on v0 and v1 before a line of code was written.
From helping users understand data context — to helping them act on it through AI
Approach
Three directions explored — one chosen for the right reason
The core design challenge was trust: AI would be taking bulk action on enterprise data assets. Users needed to feel in control, not anxious. I explored three directions before landing on the final approach.
Predictable and validated at each step, but too rigid for messy real-world instructions like "tag if PII and popularity > 80."
Rejected — too rigidMaximum flexibility and fast to ship, but no way to preview what AI would do before touching data — too opaque for governance.
Rejected — too opaqueNatural language input + structured preview before execution. Users inspect AI interpretation, see affected assets, and confirm before anything is applied.
Chosen — flexible input, structured verificationDesign Highlights
Designing guardrails for AI autonomy
The biggest design challenge wasn't the interface — it was building appropriate trust. Users needed intervention points before, during, and after AI workflow execution.
Users type intent in plain language. The agent surfaces its interpretation + a preview of matched assets before anything is applied — AI logic inspectable, not a black box.
The agent shows its reasoning live — interpreting → scanning → previewing — so users understand what's happening and why assets are included.
Low ratings (<3 stars) trigger an admin review loop — AI diagnoses gaps, surfaces fixes for approval. Failure becomes a feedback signal, not a dead end.
Outcomes & Impact
From AI answers to governed workflows
The agent platform became one of Select Star's key differentiators in late-stage sales conversations — and a contributing factor to the company's acquisition by Snowflake in December 2025.