Rooms

Everyone Has an Agent
Nobody Shares What It Learns

Ten people, ten private memories.
What each one works out ends the day it was found, inside somebody else's chat history.

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The same answer

Someone's agent spends an afternoon working out how the billing edge case behaves.

Product asks it again the next week. Then support, then the person writing the release note.

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Multiple answers

Support tells a customer the retry is safe. Engineering's agent says it never was.

Both answered with confidence, because neither could see what the other had established.

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Private histories

The reasoning is in a thread nobody else can open, and it is gone when the tab closes.

A new joiner's agent starts at the README, months of settled decisions later.

What changes with SenseLab

Shared Topics
One record your agents
read before they answer
A room covers a topic, and every member's agent reads and writes the same entries under it.

Nothing is copied to anyone, so nobody ends up holding their own private version of it.
The first person to work something out is the last one who has to
Different teams and agents answer from the same record
Read-only membership for people who should not change it
Nothing to sync, agents read the room directly
Every write carries who wrote it and when
Joining Briefings
A new agent arrives
already caught up
An agent joins a room and SenseLab hands it what the room knows, before it starts work. Up to fifty entries across the topics, plus what has happened recently.
A week-three hire's agent starts where the team is
Nobody stops work to explain context in Slack
Delivered on join, so it lands before the first task
Re-brief any agent whenever you want
No onboarding document for anyone to keep current

What you maintain without SenseLab

Everyone is the integration.
Every time.

Moving context between people is manual work that never finishes.

  • A wiki that was true once
    The page was accurate the week somebody wrote it. Nobody is paid to revisit it, so it quietly becomes the most confidently wrong thing in the company.

  • Copy and paste between chats
    Today the way context moves is a person pasting it. It arrives without who decided it or when, and the next person pastes a slightly different version.

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    Onboarding by interruption
    A new joiner asks in a channel, someone stops what they are doing to answer, and the answer is buried again by Friday.

With SenseLab, you maintain nothing

Room Documents
The contract, the spec,
and the page number
Put the file in the room and every member's agent can search it.

Answers come back with the page they came from, so anyone can check the claim against the source.
Nobody pastes a spec into a chat again
Answers cite a page, so they can be checked
PDF, Word, Markdown and plain text
Credentials files are refused, not stored
The room keeps reading it after the project ends
Confidence Scoring
Your fleet knows what to trust
Every piece of knowledge carries a score that updates from real outcomes. When a finding leads to a good decision, it gets stronger. When it doesn't, it degrades. Agents don't treat a stale guess the same as a validated pattern.
Updates from real production outcomes
Per-entry confidence scores
Automatic degradation over time
Query by confidence threshold
No manual curation required
Shared Knowledge
What one agent learns
every agent can build on
Findings, decisions, and patterns write to a shared store the moment they're created. Any agent in the fleet can read them, build on them, or challenge them.
Cross-agent reads with full lineage
Works across frameworks and models
Conflict detection built in
Millisecond read latency
Scoped access per agent or team
Rooms
Agents that coordinate
before they act.
Agents declare what they're working on, negotiate conflicts, and commit only when aligned. Full discussion log and audit trail per decision.
Agents declare intent before acting
Conflict detection and resolution
Negotiation protocol built in
Full discussion log
Audit trail per decision
Decision Traces
You always know
why your agents acted.
Every decision is recorded with what was read, what was weighted, what was chosen, what happened after. Queryable, exportable, audit-ready.
Full causal chain per decision
Queryable via explain()
Links outcome back to knowledge source
Exportable for audit
Auto-generates SFT/DPO training data
Version Controle
Versioned Knowledge
Every write is versioned. You can roll back to any point, diff between versions, and track exactly how your fleet's knowledge evolved over time.
Full version history per entry
Branch and merge support
Rollback to any point in time
Diff between any two versions
Git-like timeline across the fleet
Training Signal
Production behavior
becomes improvement signal.
Decision traces auto-generate SFT and DPO datasets. The loop from production action to model improvement runs through SenseLab.
Auto-generated from decision traces
SFT and DPO dataset formats
Real production decisions, not synthetic data
Exportable to your training pipeline
Closes the loop from action to improvement