The expensive part is rarely the conversation. It's making intelligence useful, reliable and connected to real work.
An AI agent can be a simple assistant answering familiar questions, or a custom workflow that reads information, prepares a task and works with your existing systems. Those are very different projects, even if both are called an agent.
The use case comes before the price
Start by identifying one task. Who does it now? How often? What information does it need? Which parts are repeatable, and which decisions must stay with a person? The answers determine the actual technical scope.
A frequently asked question assistant may need only approved source content and a clear handover. A system that reads unstructured requests and drafts quotes may need business rules, CRM access, testing and approval controls.
What you're paying for
The main work usually covers five things: understanding the process; connecting reliable information sources; designing the agent's boundaries; testing common and exceptional cases; and making it maintainable.
Model usage, hosting and external tools may add recurring charges. The amount depends on traffic, tool providers, data handling and how much support is required. Separate those running costs from the one-off design and implementation budget.
Start small, make it useful
A narrow, clearly defined agent is easier to test and improve than a system promised to run your business. Let the first version prepare work for human approval. Only expand autonomy when you can justify the controls and measure how it behaves.
How to judge the return
Look at time saved, response quality, reduced handovers and the effort of ongoing maintenance. An agent is a good investment when it improves a real process, not simply because it uses AI.