An agent is software that decides what to do next. That is useful and it is risky, and the difference between the two is almost entirely how you scope it.
Start from the reversible work
The first agent we build for a client never touches money, contracts or customer data on its own. It drafts, sorts, enriches, prepares. Work a person can check in seconds and undo in one click. Once the failure modes are understood, the scope widens on purpose rather than by accident.
Tools, not magic
An agent is only as capable as the tools you give it, and each tool is an ordinary API call with ordinary permissions. We write them narrow: one job, typed inputs, explicit error returns. A broad tool with a vague description is how an agent ends up doing something nobody asked for.
Where the human sits
Every agent we ship has an approval boundary written down before the first line of code. Some steps run unattended. Some queue for review. Which is which is a business decision, so it is yours, not the model’s.
What we build
- Back-office agents that triage, route and enrich records
- Research and monitoring agents that watch a source and report changes
- Delivery agents that prepare a change and open it for review, never merge it
- Internal copilots wired to your own systems rather than to a public index
Observability or it did not happen
Every run is logged with its inputs, its tool calls, its cost and its outcome. You can replay any decision. Without that an agent is an unaccountable employee, and those are hard to keep.
The honest limits
Agents are bad at long unsupervised chains. They drift, they retry, they burn budget. We design for short loops with checkpoints, and we tell you when a scheduled job would do the same work more reliably for a fraction of the cost.
Related: LLM development services for the retrieval and evaluation layer underneath, and AI chatbots and assistants when the interface is a conversation. The full picture is on the AI map.