AI agents for business, under your control.
An autonomous workforce is not one large model answering questions. It is a set of small agents, each with a single job, a defined set of tools, a spending cap and a person who owns it.
They run around the clock. What they are allowed to do without asking is something you set, workflow by workflow.
What an autonomous workforce is
Every agent is scoped to one workflow — qualifying an inbound lead, reconciling an invoice, chasing a missing document — and given only the tools that workflow needs. An agent that drafts replies has no access to the payment system. An agent that reconciles invoices cannot email anyone.
That narrowness is the point. A small agent with three tools can be reasoned about, tested against real cases and switched off without taking anything else down with it. One agent with thirty tools cannot.
First deployments go where the work is repetitive and a mistake is recoverable: inbound lead qualification, invoice follow-up, a daily operating report assembled from systems that do not talk to each other, and coordination between teams. They prove the control model on work you can still check by eye.
How it works
- 1
Map
We follow a workflow end to end and write down every decision a person makes, including the ones nobody documented.
- 2
Scope
One agent, one workflow. Tools, data access, spending cap and approval tier are all set before anything is built.
- 3
Shadow
The agent runs against live work and proposes. Nothing leaves the building. You read its output next to what your team did.
- 4
Release
Approval tiers loosen only where the shadow period earned it. The audit log and the kill switch are live from the first day.
Approval tiers, audit log, kill switch
Approval tiers decide what an agent may do alone. The lowest drafts and waits. The middle acts inside limits you set — a reschedule inside a window, a credit under a threshold — and reports what it did. The highest is for work that never leaves your systems. Anything reaching a customer, a payer or a bank starts at the lowest and stays there until you move it.
The audit log records every run: what the agent saw, which tools it called, what it produced and who approved it. It is searchable and exportable, because a log you cannot query is a log nobody reads.
The kill switch stops every agent at once and lands in under two seconds. It is on the dashboard and it is a command in the channel the agents report into, so whoever notices a problem does not have to find a laptop first.
The same standard covers everything we build — the three systems are on our services page.
Common questions
How is this different from workflow automation tools?
A rules engine does what it was told. An agent reads the situation, decides which of its tools applies, and explains the choice. That is worth having where the inputs vary — a supplier email, a denial reason, a half-filled form — and worth avoiding where a rule already works. We use rules where rules are enough.
What happens when an agent gets something wrong?
It is caught at the approval step, where a draft sits until a person releases it. The run is in the log with its inputs, so the failure is reproducible rather than mysterious, and the fix is usually a narrower tool or a sharper instruction rather than a new model.
Can we stop everything without calling you?
Yes. The kill switch is yours and it does not route through us. Agents halt where they are, queued work stays queued, and nothing resumes until someone on your side turns it back on.
One workflow first. The rest follows what it proves.
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