Use cases

Patterns for putting Instadash to work in agent pipelines — where the grid sits between agent runs, human review, and downstream action.

Human-in-the-loop review for AI agent output

Some agent outputs need a human to approve before action — outbound emails, refunds, ticket triage. Instadash gives you an editable shared grid: the agent writes rows, humans tick approval columns, the agent reads back exactly which rows changed and acts.

Persistent storage for AI agent structured output

AI agents that extract or generate structured data need somewhere to put it. Instadash gives the agent a hosted grid at a stable URL — push JSON or JSONL once, then read it back via API, MCP, or markdown without re-running the agent or re-streaming tokens.