AI agents that run the workflow, not just the conversation.
An AI agent is a system that reads your live business context — email, calendar, CRM records, tickets, documents — decides what should happen next, and takes the action inside your existing systems under a permission model you control. Unlike a chatbot, it works when nobody is typing. Every action that leaves your company waits at a human approval gate.
- Agents earn their place where the work is high-volume, rules-heavy and already leaves a digital trail.
- A Proof Pod puts one agent live on your real data in 3–6 weeks, with the metric baselined before any code is written.
- Nothing sends, posts or closes without an approval gate you configure. Autonomy is a dial, not a default.
Six places an agent pays for itself in a quarter
Patterns we see repeatedly in 250 to 1,000+ person companies. Your version will differ in the details; the shape rarely does.
The deal desk that keeps itself current
Reps stop typing. The agent reads the thread and the meeting, infers stage, value and next step, and writes it back with the exact line it inferred it from. Deals going quiet get flagged before the forecast is wrong.
Prior-authorization chase
The agent watches the intake queue, spots the missing clinical documentation before submission, drafts the payer follow-up, and escalates only the cases where the denial reason isn't in the playbook. PHI is redacted before any model call.
KYC and periodic review refresh
Renewal dates are monitored, document requests go out on schedule, returned files are checked against the requirement list, and anything ambiguous routes to an analyst with the gap named rather than the whole file re-reviewed.
Exception handling on shipments
Carrier updates and inbound email get read together. When a delay breaks a customer commitment, the agent proposes the re-book, drafts the customer notice, and holds it for a human before it goes. Clean shipments never reach a person.
Supplier quote normalization
Quotes arrive as PDFs, spreadsheets and email bodies in a dozen shapes. The agent normalizes them into one comparison, flags the line items that moved since last quarter, and routes outliers to the category manager.
Demand signal to allocation
A request for a role becomes a triaged decision: is this internal bench or external hire? The agent generates the service order, ranks candidates with reasoning attached, and routes approvals to close. Built and running today at enterprise scale.
Five mechanisms, in this order
- Triggers, not promptsThe agent wakes on an event — an email lands, a record changes, a timer fires — so the work happens whether or not anyone opens a tab.
- Typed tool callsEvery action the agent can take is a defined function against your API with a strict schema. It cannot invent an action that doesn't exist.
- Approval gatesAnything customer-facing or irreversible stops for a human. You set which classes of action are automatic and which are held.
- Evaluation on decisions, not textWe build a labelled set of real cases and measure whether the agent made the right call, not whether the sentence read well. That set becomes the regression gate.
- Observability and fallbackEvery run is traceable to its inputs. When confidence drops or a tool fails, the work routes to a person with the context attached rather than failing silently.
Two agent systems, in production
Client names and locations are withheld; figures are drawn from delivery records and approved business cases.
A CRM that fills itself in
Agents that read Gmail and Calendar, infer deal stage and momentum, cite the exact line behind every value, and hold every outbound action for human approval.
Delivered by a Production Pod
Read the full case →Automating a global talent supply chain
Demand-signal triage, automated service orders, explainable candidate ranking and approval-routed allocation through to purchase-order closure, across a 300,000+ employee firm.
Delivered by a Scale Pod
Read the full case →Three workflows we will tell you not to build
- Work with no digital trail. If the decision lives in a hallway conversation and never touches a system, there is nothing for an agent to read. Fix the process first.
- Judgment calls with no ground truth. If two experienced people would disagree on the right answer and neither is checkable, you cannot build an evaluation set, and without one you cannot know the agent is right.
- One-off analysis. Volume is what pays for an agent. A task that runs four times a year belongs in a spreadsheet and an afternoon.
FAQ
What to ask before you build an agent
What is an AI agent, in business terms?
A system that reads your live business context, decides what should happen next, and takes the action inside your existing systems under permissions you control. A chatbot answers when asked. An agent works on a trigger, whether or not anyone is at a keyboard, and every irreversible action waits at an approval gate.
How is this different from RPA or a workflow automation tool?
RPA follows a fixed script and breaks when the input changes shape. An agent reads unstructured input — an email thread, a scanned form, a call transcript — and handles the variation. Where the rules are stable and the inputs are clean, RPA is cheaper and we will say so.
Can the agent send email or update records without a human?
Only if you decide it should. Autonomy is configured per class of action. Most first deployments run every customer-facing action through an approval gate, then loosen specific classes once the evaluation set shows the agent is right often enough to trust.
What does the first engagement look like?
A Proof Pod takes one workflow live on your real data in 3–6 weeks: Pod Lead, AI solution architect and one or two AI-augmented engineers. You get the working agent, the metric baselined, an architecture decision record and a production roadmap you can defend to a board.
Most AI pilots die in the demo. Ours don't.
We define one measurable metric before we write a line of code. If it hasn't moved inside the impact window, the pod keeps building until it does, at no additional fee. You never pay for AI that just sits there.
Scope a Proof Pod →One workflow. Real data. A number that moves.
Bring the workflow that eats the most hours. We will tell you in 30 minutes whether an agent is the right shape for it.
Scope a Proof Pod →