Knowledge

Knowledge is the searchable memory of your workspace. It is how an agent in a meeting can find the decision another team made two months ago instead of re-deriving it.

Knowledge is not the company profile

Both are things agents read, and they play different roles.

	Company profile	Knowledge
What it is	The canonical description of your company	Everything the workspace has produced
How it changes	Only with your approval	Automatically, as work happens
Shape	Structured fields	Passages, searched by meaning
Authority	Ground truth	Evidence, quoted with its source

Put simply, the company profile says what is true about you. Knowledge says what has been said and done.

What gets ingested

As work happens, the platform indexes it. The corpus holds:

• Meeting messages and meeting summaries
• Tasks, with their specs
• Workspace items, the documents and notes kept in the workspace
• Research findings from research jobs
• Content assets produced by marketing work
• Artifacts created in meetings and runs
• Repository files, for repositories that have been indexed

Ingestion follows the work rather than a schedule: when a meeting summary is generated or a workspace item is updated, that change is what triggers the indexing, and deleting the source removes it from the corpus.

Everything is scoped to your workspace. A search never crosses into another workspace’s corpus, and where an item belongs to a project, a search can be narrowed to that project.

knowledge_search

knowledge_search is the tool agents use to query the corpus. It takes a question in plain language and returns the passages that answer it, with what they came from.

The same tool appears on every surface where an agent thinks, and it is the same contract each time: in meetings, in conversations with Eva, and inside coding runs, where it is exposed to the coding agent through a small local shim. See knowledge in runs.

Agents are pointed at it for a specific purpose: check whether something already exists before rebuilding it. A search returns up to twenty passages, eight by default, ranked by relevance rather than recency.

Next steps

• Knowledge search: searching the corpus yourself.
• Knowledge in runs: how a coding agent reaches the corpus.
• The company profile: the canonical context that sits beside it.