The Human-Agent Operating Layer
One layer underneath every AI tool your teams already use.
Your people are already working with AI. What they lack is anything underneath it: a shared understanding of your systems, one governed way to act inside them, and a record of what worked.
See it working
What it looks like when it is running.
Six surfaces your team would use in the first week. Open any capture at full size.
A living company brain
Your accounts, people, services, documents and procedures, entity resolved and cross linked, re-curated from every connected source rather than loaded once and left.
Answers cited to your systems
Why is this account at risk, and who owns the renewal. Answered from the brain with the owner, the lost champion and the usage drop, each traceable to the row it came from.
Skills compound across the team
A versioned registry your people publish to, fork from and deploy. The tenth teammate starts from the ninth's best work rather than from an empty prompt.
It learns from corrections
Agents propose learnings from real runs and a person approves or rejects before anything enters the company brain. Compounding that someone signed off on.
Describe an app, and it ships
A plain English brief becomes an internal app on live data, here a view of deals at risk with drafted nudges and a person approving before anything sends.
Every run traced and costed
Runs from every harness in one place, opened down to phases, tool calls and spend, so who ran what and what it cost are answerable questions.
Demo data throughout. The product is real; the company in it is invented, so nothing here needs anyone's permission to show.
What installs
One substrate, with your tools above it and your systems below it.
Everything above the middle band is replaceable by you, and everything below it stays where it is.
The six capabilities
Most teams ship one of these. They only work as one system.
You may already have two of them. What makes an agent safe enough to leave running is the wiring between them, which is why they are built and governed together.
Company Braincontext, profiled
Every source into one graph that re-profiles itself and escalates what it is unsure about.
live
Skills libraryknow-how, versioned
How your company does a thing, written in plain English, versioned and shared.
live
Agent builderplain English
Describe an agent, assembled from skills the team already published, running against the same brain and the same governed tools.
live
App buildersandboxed
Internal apps and live views on real data, scanned at build and deployed to a sandboxed URL behind the egress proxy rather than to somebody's personal account.
live
Governed accessone door
Separately revocable credential surfaces, roles across every transport with denials audited, tool allowlists, parameter contracts, egress control and a kill switch, applied the same way to a person, a tool and a scheduled agent.
live
Learning loopcorrections, promoted
Every run traced. Every correction reviewed and promoted so it reaches every agent, rather than dying in a chat window.
live
The test that matters
Take one vendor away and see what survives.
Every layer in an AI stack claims to be neutral, so the word is worth nothing. Remove each piece in turn and ask what your company still has on Monday, including us.
Where the work actually is
Connecting data and calling a model is the easy half.
These five are what stand between an agent that demos well and one you would let near a real customer.
01
Keeping context current
Schemas drift, definitions change, documents go stale.
common outcome: a one time setup, then decay
02
Who authors the rules
The knowledge that makes an agent correct sits with your ops leads and analysts.
common outcome: engineers relay it, imperfectly
03
Agents that actually act
Reading is safe. Writing to a CRM, a ledger or a live campaign is where the blast radius starts, and it needs one door with a log on both sides of it.
common outcome: keys in a chat window, no audit
04
Earning autonomy
Nobody turns an agent loose on day one. What is the gate, who sets the threshold, and what is the rollback when a change makes it worse.
common outcome: no gate, and a judgement call
05
Learning from what ran
Every run and every correction is signal. Stored and never read, it is a log. Reviewed and promoted, it is the reason the same agent is better next quarter.
common outcome: traces kept, never used
What it connects to
Your existing stack, connected rather than replaced.
Connectors are read scoped before anything becomes callable, and a system with no connector is reachable through its own API as a declared tool.
See it against your own systems.
A walkthrough on a schema and a workflow you already know well, so you can tell within minutes where this fits what your team is trying to build and where it does not.