Agentic AI Workflows

AI inside the process

An agentic AI workflow is a pipeline where the AI makes decisions inside the work, not beside it. A document arrives. The workflow classifies it, extracts the fields that matter, checks those fields against your records, and sends on the cases that pass. Only the exceptions reach a person.

That is different from a script that moves data from one box to another, and different from a chatbot that waits for someone to ask a question. The workflow runs when the work arrives. The decision is part of the path, and the path is written down so you can see why a case was accepted or held.

Classification, extraction, and validation are the core of these builds. Classification decides what kind of item this is. Extraction pulls the values your process needs. Validation compares those values with the records you already trust. If the comparison fails, the case escalates. If it passes, the workflow continues.

Where a person still belongs

Routine cases should not wait for a person. Exceptions should not be forced through. We agree the boundary before the build. A missing field, a value that does not match the record, a document type the workflow has not been shown, or a case above a threshold you set: those go to someone on your team, with the reason attached.

The person who receives an exception should not have to start from zero. They see the classification, the extracted fields, the check that failed, and the source record. Their job is the decision, not the retyping.

This is also how you keep a workflow honest after launch. If every case escalates, the checks are too tight or the inputs are not what we were shown. If nothing escalates, the checks are too loose. Monitoring is part of the handover so you can see which of those is happening.

How a workflow is built

We start from the process you run today, not from a model. The discovery call maps the types of work that arrive, the records they must be checked against, and the people who handle exceptions. The proposal after that call has a fixed scope, a timeline, and a price.

The build uses your real documents and your real records. Weekly demos show the classification, the extraction, and the cases that were held back. We do not demo a clean sample and then discover the messy files in the last week.

The workflow writes back to the tools you already use. It does not ask your team to live in a separate inbox. Launch includes documentation of each decision point, so a new person on your side can see why a case moved forward.

What we need from you

We need examples of the real inputs, including the ones that are incomplete, and the records those inputs are checked against. We need the person who currently decides the exceptions, because that person knows the cases a rule will miss.

We also need agreement on what must never be auto-accepted. If that line is not clear, we do not pretend a workflow can draw it. GDPR and HIPAA requirements stay in scope when the records include personal or health data, and the workflow can run on your own infrastructure.