Agentic automation
Traditional automation handles processes with clear rules. This is for the ones with a judgement in the middle, which is why they were never automated.
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The processes rules could not reach
Every company has work that was left manual because the rule was never writable. Emails into a shared inbox that need reading before anyone knows where they go. Supplier documents in a dozen formats. Customer messages where the urgency depends on tone as much as content. Support tickets that need categorising before they can be routed. These resisted automation because the first step is comprehension. A model can do that step, which means the rest of the process, which was always automatable, finally becomes reachable.
Judgement narrow, actions explicit
The reliable pattern is to keep the model's job small: read this, classify it into one of these categories, extract these fields, decide which of these three paths applies. Deterministic logic does everything after. That keeps the unpredictable part contained and the rest testable. What we avoid is handing an agent broad autonomy over a business process and hoping. Every action is scoped and logged, uncertain cases go to a person rather than being guessed, and anything with financial or contractual consequence requires approval. A workflow that escalates ten percent of cases and handles ninety correctly is a success; one that handles everything and is wrong occasionally in ways nobody notices is a liability.
Built on what you already run
Usually Power Automate where you are a Microsoft company, with the model called as a step. Azure Functions where the logic outgrows a visual designer. n8n or a similar tool where you want self-hosted workflow automation you control entirely. We pick based on what you already operate and who will maintain it after we leave, not on what is interesting to build. An elegant automation nobody in your company can modify is a dependency, and dependencies have a habit of becoming urgent at the worst time.
What you get
Handles unstructured input
Email, documents and messages that needed reading before anyone knew where they went.
Model's job kept small
Classify, extract, choose a path. Deterministic logic does the rest, so most of it stays testable.
Uncertain cases go to a person
Ninety percent handled and ten percent escalated beats a hundred percent handled and occasionally wrong.
Every action logged
What it did and why, so a decision can be reviewed rather than taken on faith.
Approval where it counts
Anything with financial or contractual consequence is proposed, not executed autonomously.
Built on what you run
Power Automate, Azure Functions or self-hosted. Chosen for who maintains it, not for what is fun to build.
Technologies
Frequently asked questions
How is this different from Power Automate?
It usually is Power Automate, with an AI step where the process needs comprehension. The distinction is not the tool but the type of work: traditional automation needs a writable rule, this handles the cases where the first step is reading and understanding something.
How do we know it is making good decisions?
Every decision is logged with its reasoning, and we start with the agent proposing while a person approves. Once the evidence supports it, you can raise the confidence threshold for straight-through processing. Trust is built from data, not granted at launch.
Where should we start?
With a high-volume process where errors are recoverable. Routing incoming email or classifying tickets are good first cases: frequent enough to matter, and a misrouted message costs minutes rather than money. Payments and contracts come later, once the pattern is proven.
Automate the work that needed a judgement first
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