Agentic Workflows and Business Applications
The engineering of action: tools, approvals, exceptions, reversibility and maintainability.
What this collection argues
Cover the difference between answering and acting, and the practical mechanics of building applications that remain maintainable long after the pilot team has moved on.
In this collection
5 articles. Each one opens with the business problem, names the executive who owns the outcome, and ends with the metrics that would show whether the work paid.
From Chatbot to Business Execution: What Changes When AI Can Take Action?
Moving from answering questions to executing tasks changes the unit of risk from a wrong sentence to a wrong transaction, and that shift has to be designed for rather than discovered.
Designing AI Workflows with Human Approval and Exception Handling
Approval gates, escalation, reversibility and audit belong inside the workflow design itself, and a workflow without a defined exception queue is an operational liability rather than an automation.
When to Use AI, Conventional Automation, Business Rules or a Combination
Most operational tasks are better served by a rule or a script than by a model, and an AI component earns its cost only where input variation defeats fixed logic and the consequence of error is bounded.
From Business Requirements to Structured Applications Without Endless Custom Development
Business applications built from defined objects, forms, permissions, workflows and tests can be delivered in short iterations, provided the structure is settled before the interface is designed.
How to Make Enterprise AI Maintainable After the Pilot Ends
An AI system becomes maintainable when it is owned as a product, tested against business expectations, monitored against the data it actually sees, and versioned in its rules as carefully as in its code.