Autonomous Agent Systems
An agent, in this context, is just software that takes a goal, decides on the next step it ought to take, and then acts on that decision. It's useful for work that involves judgment and a sequence of actions. It also gets dangerous quickly when the system can do whatever it wants. Our view is that agents only really work in production when they're bounded properly.
What we build
The agents we deploy handle multi-step tasks like triaging tickets, processing inbound documents, running research workflows, or orchestrating data across several tools at once. They do all of this within an explicit policy. The agent only ever sees the data you've authorized, and it only takes the actions you've approved as part of the workflow. Anything outside of that envelope, it escalates to a person.
The rules we enforce
- Scoped data access. The agent reads from the systems you've connected, and nothing further.
- Explicit action set. Every action is a defined operation rather than a freeform decision. Adding a new action requires a deployment, not a clever prompt.
- Human-in-the-loop where it matters. Anything that moves money, changes records of consequence, or sends an external message can be gated on human approval.
- Full audit log. Every step the agent takes gets recorded alongside the input that produced it, so you can replay any decision after the fact.
What it doesn't do
The agent doesn't make up policies of its own. It doesn't decide unilaterally to add new tools to its toolkit. It doesn't reason in directions that haven't been sanctioned by you. The point of bounding the system this carefully is that you know roughly what the failure modes will look like before you turn the thing on.
If you've got a multi-step process that looks like it might be a candidate for an agent, let's talk it through.