Agent R
The context keeper.
Decisions, designs and agent work, connected in one shared memory. The boards follow the work.
Interactive example · fictional workspace
01 / The context behind the work
A decision in chat. A delivery without proof. Two sources that disagree. Give the next person the context behind the work.
The conflict is recorded. Maya’s ruling becomes the reference for the design and the next handoff.
The original approval decision stays attached to the ruling. The next person can see where the rule came from.
The answer survives the conversation.
02 / A shared way to work
Shared context. Local execution.
You keep the judgment.
The context keeper.
The local doer.
The goal, requirements and past decisions travel together.
Agents produce. Humans author.
No local agent? Contribute directly through R.
03 / Yours from the start
One shared memory.
Clear boundaries around it.
Agent R runs in your organization's own deployment, with its own database, storage and versioned workspace knowledge. Approved contributions go into that shared space.
Agents surface conflicts and prepare the work. The decision owner makes the ruling. Agent L asks for approval before filing; changes to your product still need human review and merge.
Your consent settings govern personal session traces and authored prompts. They are separate from the contributions you approve for your organization.
Meeting Pal captures meetings locally, including transcripts and recordings. Carry the relevant work forward deliberately; raw meeting capture is separate from the shared organization record.
Make room for what comes next
Your people, your agents, and the context they need.
Bring them to the same place.