Spark AI takes action: from answers to agent

Until now, Spark AI was already doing its job well: answering your questions from your project’s knowledge, through a retrieval-augmented generation (RAG) system specific to each workspace. You asked, Spark AI searched your notes, documents and transcripts, and answered.

That approach has a limit anyone who has used an AI assistant knows: when the question is ambiguous, or when the answer takes several steps, the assistant makes an assumption on your behalf — and sometimes gets it wrong.

That’s what this update changes: Spark AI becomes agentic.

What actually changes

Spark AI now has two new capabilities:

1. Tools. Spark AI no longer just searches a text index: it can now look up tasks, cross-check sources and verify a piece of information before answering, instead of stopping at a single search pass.
2. The ability to ask questions. When a request is ambiguous — “prepare a summary for the client” without saying which one, or “schedule a meeting” without saying with whom — Spark AI asks you, instead of guessing and producing an answer that misses the point.

Why it’s more than a technical improvement

An assistant that misreads your intent produces a fast answer, but a wrong one — which often costs more time than answering yourself would have. An assistant that asks the right question before acting answers a few seconds later, but reliably.

It’s a change of stance: Spark AI goes from conversational search engine to a collaborator that checks before it asserts. In practice, that means more accurate answers on complex requests, fewer back-and-forths to fix an answer that missed the point, and better reliability when several sources of knowledge have to be cross-checked to answer correctly.

Still grounded in your project knowledge

This evolution doesn’t change what makes Spark AI strong: every answer is still built from your project’s own knowledge — your meetings, your documents, your decisions — not from generic knowledge gleaned from the web. Agentic Spark AI simply goes further in using that knowledge, with more rigor before it answers.

It’s the same logic that shapes all of 5DAYS: your team’s knowledge (layer 01) should turn into reliable decisions and actions (layer 02) — not plausible approximations.