The cost of building an AI agent depends mostly on six things: how complex the job is, how many systems it has to connect to, how much protection and checking it needs, how much it uses the AI model, how well it is tested, and how it will be maintained after launch. The AI model itself is often a smaller part of the total than people expect.
1. The scope of the job
An agent that handles one narrow task is very different from one that is expected to handle a whole department. What drives the effort is the number of steps, the number of exceptions, and how clearly the process is defined today. If nobody can describe the process step by step yet, part of the work is figuring that out first, before anything is built.
2. Integrations with your systems
Every system the agent needs to read from or write to has to be connected: a spreadsheet, a customer database, a chat app, an order system, an internal tool. Each connection needs access, error handling, and testing. Systems with a proper API are straightforward. Older systems without one, or ones that need workarounds, take noticeably more effort.
3. Safety, permissions, and checks
The more an agent is allowed to change, the more protection it needs: deciding which actions need approval, setting limits on where it can work, keeping a record of what it did, and checking its output before it reaches anyone. An agent that only reads and summarises needs little of this. One that updates records, sends messages to customers, or touches money needs a lot.
4. Ongoing model usage
Most AI providers charge by usage, so running costs depend on how often the agent works and how much text it reads and writes each time. Two design choices make a real difference: using a lighter, cheaper model for small background tasks while keeping the stronger model for the main work, and having a backup provider so the agent keeps running when one has problems. In the agent we built, both made running costs far more predictable.
5. Where people use it
An agent that works inside a tool your team already uses, such as a chat app, can be simpler to deliver than one that needs its own dashboard or interface. A custom interface adds design and development work, but it can be worth it when several people need to review or approve what the agent does.
6. Testing
A reliable agent needs a set of real tasks with clear pass or fail checks, run every time the instructions, tools, or model change. Building that test set takes time at the start, but it is what lets you change things later without breaking what already works.
7. Maintenance after launch
AI models get updated, providers change their offerings, and your own processes evolve. An agent needs someone to keep it working and to improve it as you learn where it helps most. It is worth planning for this from the beginning rather than treating launch as the finish line.
How to keep the cost under control
- Start with one job that repeats often and has a clear definition of done.
- Connect only the systems that job needs, and add more later.
- Begin with close supervision, then reduce it as the results prove reliable.
- Measure the time it saves before expanding it to other tasks.
We don't publish fixed prices, because every agent is different. Tell us the job you have in mind and the systems involved, and we'll explain what it would take before anything gets built.
Share a short description of your business and what you want to improve. We'll get back to you.