AI workflow automation.

Put interpretation where it helps

Documents and messages rarely arrive in one convenient format. AI can be considered for extracting information, organizing material or drafting a response. The important question is what happens afterward: which details can be checked, which remain uncertain and what a person needs to decide.

Possible starting points

• A document-intake workflow that proposes captured fields, checks arithmetic and routes an exception for review.
• A research assistant that gathers approved sources and prepares a traceable summary for a named decision.
• A bounded agent that uses approved tools to prepare a handoff, with consequential actions held for review.

Give the agent a job and a boundary

An agent needs a defined task, permitted sources and tools, a stopping point and a way to recover from errors. We agree on those before connecting real business systems. A proposed field, draft or action stays distinguishable from something a person has reviewed or a destination has accepted.

Tools such as Codex or Claude may be considered for development or agent work when they fit the task and approved data boundaries. The tool choice follows the need; it does not establish permission to access a source or take an action.

Make the human decision useful

Review should show the original evidence, the proposed result and the reason a check needs attention. A person needs to be able to correct a value, return the work or approve the next prepared step. The scope identifies who makes that decision and what can happen afterward.

Test beyond the ideal example

We test incomplete and contradictory inputs, incorrect suggestions, unavailable services and repeated requests. Expected outputs, review points and failure behavior are agreed for the first phase. The sandbox lets your team try the workflow, comment on the result and verify fixes before acceptance and release.