AI and Automation
Agents that act within limits you can see
Agentic AI plans and carries out multi-step work on its own. We design the governance that decides what it may do, and build the systems that keep it there.
More capable means more careful
An AI agent does not just answer a question. It takes actions: it reads systems, calls tools, updates records and hands work to other agents. That is what makes it useful, and it is also what makes it risky. An agent with broad access and vague instructions will eventually do something nobody intended, quickly and with confidence.
Most agentic AI failures do not come from too much control or too little. They come from never deciding what the agent is for, what it may touch, and who checks its work. Constraint is what gives an agent a useful shape, and governance designed in from the start is what lets you give it real work.
Governance you can enforce, not just document
A policy that says an agent should not touch production is a request. Credentials that cannot reach production are a fact. We design agentic systems around least privilege, so the limits are enforced by the architecture rather than by the model remembering its instructions. Then we add the review points, logging and audit trail that let people see what an agent did and why.
We evaluate agent platforms the same way. Architecture documents describe intent, so we look at what a runtime actually enforces before recommending you trust it with your systems.
How the work runs
1Define the job
We decide what the agent is for, what success looks like, and which decisions stay with a person.
2Set the boundaries
We define what the agent may read, change and call, and enforce those limits in the architecture.
3Choose the platform
We evaluate agent runtimes on what they actually enforce, not on what their documentation promises.
4Build with oversight
The agent ships with review points, logging and an audit trail from its first version.
5Operate and audit
We monitor what the agent does in production and review its work on a regular schedule.
What you leave with
An AI governance model covering purpose, permissions and human review points
A least-privilege access design for each agent
An evaluation of candidate agent platforms
A working agent with logging, monitoring and an audit trail
An operating and review plan for production
Our thinking on agentic AI
Framing Freedom: A Governance Model for Agentic AI
Most AI failures are not about too much control or too little. They share one root: nobody understood that constraint is what gives agency its shape.
Guarding the Way: How to Actually Evaluate an AI Agent Runtime
You cannot evaluate an agent runtime by reading its architecture docs. A security invariant in a comment is a request. In the type system, it is a fact.
Honest answers
What is the difference between an AI assistant and an agent?
An assistant answers when asked, and a person decides what to do with the answer. An agent carries out multi-step work on its own, using tools and systems to get there. That independence is the value, and it is why permissions and oversight matter so much more.
Do we need an AI governance policy before we start?
You need the decisions a policy records: what AI may be used for, what data it may touch, and who reviews what it produces. We help you make those decisions against real use cases, then write them down in a form your teams can follow.
Is it safe to let an agent work in our systems?
It can be, when its access is limited to exactly what the job needs, its actions are logged, and a person reviews the work that matters. We would not recommend an agent anywhere those three things cannot be true.
Give AI real work, with real limits
Talk with us about the work you want an agent to take on, and what it would take to trust it.