Tables
A table stores structured data: columns are the types, rows are the entries. Think of it as giving your workflows a spreadsheet — or a lightweight database — for tracking and maintaining records.
What you will learn
The shape of a table
Columns define types, rows hold entries — the same mental model as a spreadsheet your workflows can read and write.
Work as operations over data
Many business use cases are just a query, an update, or a combination with logic over structured records.
Scale with workflow columns
Workflow columns run an operation across every row in parallel, making a table a surface for automation at scale.
A familiar shape
If you've used a spreadsheet, you already understand a table: columns with types, rows of entries. Your workflows read rows to work on, write rows they produce, and update rows in place.
Here's the shape of it — a workflow that reads a table, operates on each record, and writes the result back:
Operations over data
A surprising amount of real business work decomposes into a few operations over structured records:
- A query — "which leads are still unprocessed?"
- An update — "mark these rows handled."
- A combination with logic — "for each new signup, score it, then write the score back."
Once you see a use case that way, building it in Sim is mostly wiring those operations together.
A surface for scale
Tables aren't only storage — they're a working surface for your AI systems. Workflow columns let you run an operation across every row at once, in parallel, so a table becomes the place you launch and track work in bulk.
That makes a table a powerful interface for automation — the place you manage and operate agentic processes at scale.