What is an AI agent, in plain terms?
Software that carries out a defined workflow, not just a conversation. A trigger starts it; permissions and approved rules bound its actions. It can complete routine work unattended and route exceptions to people. It still needs monitoring, maintenance and a recovery plan.
Which tasks are actually worth handing to an agent?
Look across accounting, invoicing, scheduling, data capture, operations, sales, service and any other function. Frequent work with clear rules, accessible inputs and measurable outcomes is a candidate. Our five examples are not an exhaustive menu. Feasibility, risk and full operating costs decide what to automate; judgment and relationship work stay with people.
What does this cost, and how is the business case built?
Scope determines cost; we do not publish a generic rate card. Compare measured hours and fully loaded labor cost with implementation, integration, usage, maintenance, monitoring, failure recovery and human review, exceptions and residual work. Released hours can support service and revenue work, but are not automatically cash savings. A pilot tests the case before expansion.
Will this work with the systems we already run?
Discovery checks supported APIs or exports, licensing, permissions, data handling and security requirements, including integration with an existing phone system. No example is an off-the-shelf compatibility promise, working demo or deployed customer result. We report what is feasible, what needs work and what is not practical before proposing an integration.
When do people step in, and who controls the data?
Your team approves the rules and owns the decisions. Routine tasks, including permitted customer communications, may run unattended. Uncertainty, sensitive cases and high-impact actions require human review. Before implementation, agree data access, processing locations, retention, logs and named escalation owners under your policies and applicable obligations.