Agentic AI that runs the process, with the line you draw.
Your processes do not connect. Your people do, by hand, carrying information between systems that were never introduced to each other. An agent can run that path. What makes it safe is deciding in advance exactly where its authority stops.
- The autonomy line set per process, from confidence thresholds, explicit rules and guardrails you define.
- Agents run the high volume, low risk path unattended. Exceptions and risk decisions reach a person.
- Every action recorded, so what the agent did is a fact you can check rather than a claim you accept.
Book a governed AI review.
Bring one process your people are holding together by hand. You leave with the autonomy line drawn for it and an honest read on whether an agent should touch it at all.
You have automated before. This is the part the rules could not reach.
Every operation has a happy path that automation already covers. The cost sits in everything else.
The clean cases were automated a decade ago
What is left needs judgement: the invoice that does not match, the order coded to the wrong entity, the document that says one thing and the system another. That is where the headcount went.
Every new case is another branch nobody maintains
Rule-based automation is precise and brittle. It handles the cases it was told about and hands the rest to a person, which is why the exception queue never shrinks.
The real process lives in inboxes and spreadsheets
Nothing in the system of record shows the five minutes someone spent reconciling two screens. It is not measured, so it is never funded, and it is most of the work.
Your people are the middleware. They should not be.
See what it did. Bound what it can do. Answer for all of it.
Not another autonomous agent promise. The same governed model every time, with the line drawn in a different place for each process.
The agent runs the repeatable path
Grounded in the systems the process actually depends on, so it reads live business truth rather than a summary that went stale last quarter.
AgentChecked against the rule it has to meet
Confidence below the threshold, a rule breached, or a value outside the guardrail, and it stops there. The check is separate from the agent that did the work.
IndependentExceptions reach a person, with context
Your team stops doing the routine path and starts doing the judgement calls, arriving with what the agent found and why it stopped rather than a raw queue.
HumanYou set the line, per process, and you can move it.
Autonomy is not one setting for the whole business. It is four decisions, made once per process, and revisited when the evidence says to.
A repeatable operation, run by AI, at national scale.
AI in the drive-through, exceptions still reaching a person
Hungry Jack's put voice ordering into the live customer path across a national chain, handling accents and noise, with predictive ordering behind it. The figures above are from that work.
Search and inventory that stopped needing a phone call
Blackwoods, where finding the right part across a very large catalogue was the operation, and getting it wrong cost a site visit.
Score your own process in three minutes
Ten questions across the five dimensions that decide whether an agent is ready to run unattended. Verification scoring zero caps the result, whatever else is in place.
Thirty minutes, and three things you did not have before.
This is a working session, not a discovery call that ends in a proposal you did not ask for.
- Where the line should sitThe four autonomy decisions applied to your actual process, including the cases we would never let an agent touch.
- What it is worthThe volume that is genuinely repeatable, separated from the exception load, because only one of those is addressable.
- A scoped next stepThe smallest slice of the process that would prove or kill it, with what it takes to run. If an agent is the wrong tool here, we say that.
What operations leaders ask on the first call.
Exactly what you decide, per process. The four settings are a confidence threshold, the rules that always require a person, the guardrails it may never cross, and where an exception goes. Nothing is granted to the agent in general, only to the specific action in the specific process.
The guardrail, which is a boundary in the system rather than an instruction in a prompt. An instruction is something a model can reason its way around. A boundary is not. Anything that touches money, a customer commitment or a regulated field sits behind a person by default.
They arrive with what the agent found, what it was about to do, and why it stopped. That is the difference between a queue of alerts and a queue of decisions. Your team spends its time on the judgement rather than on reconstructing the context.
No, and replacing them would be a waste. Deterministic automation is better than an agent at anything with a fixed rule. Agents earn their place on the work that needed judgement, which is why the exception queue survived every automation programme you have already run.
It gets caught at the verification step, before the action reaches the process. The failure is recorded, and the pattern becomes something the check looks for. If a system never reports a wrong answer, it does not have verification, it has optimism.
Name the one process your people are holding together.
Thirty minutes on that process. We will draw the autonomy line with you, and tell you plainly if it is not a candidate.