The shift from chat to action
A conventional assistant answers. An agent can decide that it needs an order, call an order API, inspect the result and then decide whether another action is required.
Controlled agent architecture
Tool contracts remain ordinary software
public interface ICustomerOrderTool
{
Task<OrderSummary> GetOrderAsync(
long orderId,
UserContext user,
CancellationToken cancellationToken);
}The tool still validates who the user is and what data they may access. AI does not bypass the application's authorisation model.
High-value operational use cases
Support
Retrieve order state, find policy, prepare a response and create a draft ticket.
Operations
Summarise exceptions, collect evidence from systems and suggest next actions.
Sales
Prepare account context and draft follow-up based on approved CRM data.
Admin
Classify documents, propose structured fields and route work for approval.
Use approval boundaries for consequential actions
Reading order history and issuing a refund are not equivalent risks. Higher-impact tools should require stronger permission checks and often explicit human approval.
Every action should be explainable
Record which user initiated the request, which tool ran, what arguments were validated, what business object changed and what result came back.
Business value
Why it matters to the business
- Turns AI from a content tool into an operational productivity layer.
- Lets existing APIs become reusable AI capabilities.
- Can reduce multi-system navigation for staff.
- Supports controlled automation without replacing core systems.
- Creates a gradual path from assistant to workflow automation.
Practical considerations
- Tool permissions need careful design.
- High-impact actions should remain approval-gated.