Solution · AI Operations
Scale from 5 to 100 AI agents without each one bringing its own fire
One person can watch five agents by eye. Not a hundred. At that scale the problem stops being building agents and becomes running them: orchestrating, supervising, controlling cost, and containing the incident before anyone notices. That layer gets built — not improvised once you've got eighty running.
The problem
With five agents you improvise. With a hundred, the improvising is the incident.
- The first five agents get watched by eye. By thirty, nobody knows which are fine, which are failing quietly, and which have been giving bad answers for days.
- Every new agent gets built by hand — its prompt, its credentials, its logic: shipping number forty costs almost as much as building the first.
- The token bill climbs every month and nobody knows which agent is eating it, which is overworking, or which could be switched off without anyone noticing.
- When one fails in production, you hear it from the customer or the process owner — not from an alert — and there's no fast way to know how many others are doing the same.
Cost of staying the same
A fleet of agents built five at a time doesn't break on deploy day: it breaks when you go from the ones that fit in one person's head to the ones that don't. Without orchestration, every new agent is manual work that doesn't scale; without supervision, failures pile up quietly until a customer reports one; without cost control, the bill grows faster than the value. The ceiling isn't the technology: it's that running a hundred agents by hand is impossible, and most fleets stall at fifteen without knowing why.
The solution
We build the layer that runs the fleet — orchestration, supervision, cost, and quality — so shipping agent one hundred costs like shipping the sixth
- 1We put an orchestration layer over your agents: shared infrastructure, repeatable deployment, and a catalog where every agent has an owner, a version, and permissions. Shipping the next one stops being building it from scratch.
- 2We set up supervision: a dashboard where you see which agent is running, which is failing, which is drifting, and at what quality it responds — with continuous evaluation, not reviewing conversations by hand once it's too late.
- 3We bring in cost control per agent: what each one consumes, what it returns, and which ones can be pruned or moved to a cheaper model. The bill becomes a decision, not an end-of-month surprise.
- 4We close with incident management: alerts when an agent goes off the rails, a procedure to stop and roll it back, and a trail of what happened. The first warning stops being the customer.
What changes
What you stop losing
The cost of adding an agent stops growing with the fleet: with shared infrastructure and repeatable deployment, number eighty ships as a template, not as a project.
Mechanism
Failures stop living hidden until someone suffers them: continuous supervision surfaces them when they happen, not when the customer reports them.
Mechanism
The fleet's bill goes from monthly surprise to lever: you know which agent consumes what and decide what to prune without switching off value.
Mechanism
What we measure: agents in production per operator, cost per agent and per task, failure rate caught by supervision versus reported by the customer, and time from detection to containment of an incident.
What we measure
Spec sheet
- Work it removes
- running an AI agent fleet by hand: building them one at a time, watching them by eye, and finding the failures through the customer
- Typical setup
- 2–5 weeks for the layer; cost and incidents tuned in operation
- Input
- a handful of agents already in production that you want to take to dozens or hundreds without multiplying manual work
- Output
- a run fleet: orchestration with repeatable deployment, continuous supervision, controlled cost per agent, and incident management
- Works with
- Anthropic ClaudeOpenAIAzure OpenAICurrent orchestrator
- Can connect to
- Your agent stackSupervision dashboardYour models and providers
- What we measure
- agents in production per operatorcost per agent and per taskfailures caught by supervision vs reported by customertime from detection to containment
- Good fit for
- companies with several agents already in production hitting the ceiling of running them by hand, wanting to go from dozens to hundreds
- Not a fit for
- anyone with no agent in production yet, or who just wants to build the first one: that's a different job
Frequently asked questions
No. Building agent number six is something anyone can do. The hard part is fitting a hundred at once: that they don't collide, that you know which one is failing without opening a hundred conversations, that the bill doesn't blow up, and that an incident isn't discovered by the customer. That's running a fleet, not building another agent — the difference between five cars and a fleet with a workshop, routes, and control.
No. We start from your current fleet and put the operating layer on top: orchestration, supervision, cost, and incidents. The agents that already work stay; what changes is that now they're visible, measured, and deployed as a template, not one at a time.
When you can no longer hold them all in your head. With five, one person watches them. The problem shows up around fifteen or twenty: the manual work of building and watching them grows faster than the value, and that's where most fleets stall. If you already feel it, it's time; if you're about to get there, better to build the layer before you hit the wall.
Want it running in your business?
You’ve pinned the problem. We ship the fix and leave it measured.