AI agents that finish the work, with you in control.
A custom AI agent is software that can plan a task, use your tools and data, and work through several steps to get it done, such as triaging requests, preparing reports or checking documents. We build agents with clear limits, human approval for the decisions that matter, and monitoring so you can see what each one did.
What is a custom AI agent?
A custom AI agent is a language model given a clear job, a set of tools and rules about what it may do. It reads the situation, picks the next step, uses a tool, checks the result and carries on until the job is done. A person approves anything important along the way.
What you get
- An agent designed around one clear job, with defined tools and limits
- Connections to the systems it needs, such as your CRM, database or document store
- Human review steps for anything costly, sensitive or hard to undo
- A test set of real cases, run before every change
- Tracing and cost monitoring, so you can see what it did and what it cost
Who it is for
Operations, support and analyst teams handling work that needs reading, deciding and acting across several systems, where fixed rules keep breaking.
How it works
- We pick one job and agree what a good result looks like.
- We build a first version and test it against your real cases.
- You compare its work with your team's before it acts on its own.
- We launch with monitoring and widen what it handles step by step.
Not sure whether you need an agent at all? Read when a business problem needs machine learning.
Honest limits
Agents are not right for every task. If fixed rules or plain process automation will do, they are cheaper and more predictable, and we will recommend that. Agents also cost more to run per task than simple software, so we estimate running costs in the scope.
Questions about Custom AI agents
How is an AI agent different from automation?
Automation follows fixed steps. An agent decides the next step based on what it finds, which suits work that changes from case to case. We often use both together.
How do you stop it from making mistakes?
We limit which tools it can use, require approval for risky actions, and test it against a set of real cases before launch. Every run is traced, so errors are easy to find and fix.
Can it run on our own servers?
Yes. When data cannot leave your systems, we can use open models that run privately on your own infrastructure. We explain the trade-offs in cost and speed up front.
Which AI models do you use?
We choose per task from the major cloud providers or open models, and keep the choice swappable so you are not locked in.
See if it fits your business.
Bring one real lead or question your team handled this week. We will show you what this would do with it.