AI workflow automation that ships

Pick the path

High volume, clear "done," expensive exceptions, and a willing owner. Document-heavy intake is a common winner.

Avoid workflows that change weekly with no written rules.

Build the loop

Sense → reason → act → human if needed → log. Measure queue time and override rate.

Expand document types only after the first loop is boring in production.

Scoping the first path

One package type, one line of business, one queue. Write the completeness checklist before you pick a model.

If stakeholders cannot agree on "done," stop. Automation will encode the disagreement.

Expanding carefully

Add document types only when the first path is boring: stable override rate, known failure modes, trained operators.

In practice

Map the workflow on a whiteboard before you open a framework: inputs, systems of record, humans, and irreversible writes. If that map is fuzzy, the agent will encode the fuzz.

Pick ten to fifty real historical cases as an eval set. Include the ugly ones. Run the agent offline against them until critical fields and hard rules are acceptable. Only then connect write tools.

Ship with a pause switch, a human queue, and a weekly review of override reasons. Promote repeated overrides into rules. That loop is how production systems improve—not another prompt brainstorm.

Common failure modes

• Treating a demo on clean samples as readiness for production volume.
• One shared service account with broad write access across systems.
• No owner for the exception queue, so failures pile up as noise.
• Changing prompts and models without regression gates on real cases.
• Measuring only model latency or thumbs-up, not completed-case cost and audit completeness.

What good looks like after ninety days

The first workflow is boring: stable override rate, known failure modes, operators who trust the queue. Config changes go through review. Traces answer "what happened to this case?" without archaeology.

At that point you can add a second document type or a second agent role. Expanding before the first path is boring is how programs stall with five half-built pilots.