Put AI agents to work without losing control of them.
AI agents take a goal, plan the steps and act across your systems. That is where the value is, and also where a mistake stops being a wrong sentence and becomes a wrong transaction. This page is about deciding and governing: where agents belong, what they may do and how you check. The building happens in the lab.
Refund agent: what it may do
Written before the pilot starts, agreed with the workflow owner, and enforced in the systems the agent can reach.
| Action | Rule | Logged |
|---|---|---|
| Read order history | allowed | yes |
| Refund up to Rs 2,000 | allowed | yes |
| Refund above Rs 2,000 | approval | yes |
| Email the customer | template only | yes |
| Change delivery address | not allowed | n/a |
The questions to settle before an agent goes live.
Where an agent belongs
Which workflows have a business case and a result you can check, and which are better left to a person or a simpler tool.
Architecture: build, buy or wait
Agent platforms, model choice, where the agent sits among your systems, and what the running cost looks like at production volume.
Permissions and blast radius
What each agent may read, write and spend, and what enforces that limit.
A record of what happened
Logs and reviews good enough to explain an agent's action to a customer, an auditor or a regulator.
A pilot that tells the truth
One workflow, measured against how it runs today, with an agreed test for when the agent is allowed to go further.
People
Who supervises the agent, what they need to learn, and how the team's work changes.
One workflow, one free hour.
Bring one workflow you are considering to an advisory session. You leave with a view on whether an agent fits, what it would take, and the smallest pilot that would prove it.
If you need continuing senior oversight across several AI initiatives, that is the fractional Head of AI arrangement. For the engineering, including release safety testing, see agents in the AI lab.
Before you write to us.
How do you implement AI agents in a business?
Start with one workflow where mistakes are cheap to catch and cheap to undo. Write down what the agent may do and what needs a person's approval, run a pilot against a baseline with a named owner, keep a record of every action, and widen the scope only as the evidence comes in. The free book, Implementing AI Agents in Your Company, walks through each step.
Should we build AI agents or buy an agent platform?
Most companies should buy the agent loop and keep what compounds: their own corrections, review rules and data. Building makes sense when the workflow is your differentiator and you have the engineers to maintain it.
Which workflows make good first candidates?
Work with clear inputs, a result that is quick to check, and a low cost of error: support triage, document processing, reconciliation, internal research and reporting. Avoid starting with long multi-step workflows, because small error rates compound across steps.
Do you build the agents as well?
Our lab builds prototypes and, when a pilot earns it, production agents. If a vendor or another team is the better fit, we help you choose them and review their work, so the advice stays independent of who builds it.
Bring one workflow you are considering.
In a free advisory session you get a view on whether an agent fits, what it would take, and the smallest pilot that would prove it.
Book a free advisory session