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Artrilogic

Agentic AI

AI that carries out multi-step work within limits you set.

Some steps take more than one answer: checking several systems, weighing what they say, then acting. We design AI agents for that work, with tasks small enough to handle reliably, controlled connections to your systems, and the freedom to change AI provider later.

Talk to us about a processHow governance works
An example agent workflow: the agent receives a goal; it plans the work as small tasks; it uses only approved tools through governed connections with scoped permissions; the proposed action is validated; then it is executed only within categories and limits approved in advance, or prepared for a person to approve. Anything outside policy, confidence or authority goes to a named person, and every call is audited.

Illustrative example

  1. 01Goal receivedA multi-step piece of work
  2. 02Plan small tasksEach one a model call can hold
  3. 03Use approved toolsGoverned connections, scoped permissions
  4. 04ValidateBefore anything changes
  5. 05Execute or prepareWithin approved limits, or for approval

Outside policy or authority: a named person decides

01What you are trying to do

You want AI to take on a multi-step process, and the first prototype looked promising.

It impressed everyone in the room. In production it loses track of where it is several steps in, and nobody can say what it did or why.

02What gets in the way

An agent wired straight into your systems can become a single point of failure for the work it touches.

Without controls at the point where it connects to your systems, an agent that misfires can cost real money or send the wrong message to a customer before anyone notices.

03How we help

We split the work into small, checkable tasks and prepare anything that cannot be undone for a person to approve.

This is ordinary engineering discipline applied to a capable technology. The result is an agent your team can run a process on during the week and show an auditor at the end of it.

How we build them

Four engineering principles.

The model provides the capability. Reliability comes from how the work around it is designed, which is what these principles cover. Often the right answer is a workflow engine and no agent at all, and we will say so.

  • Break the work into tasks the model can hold

    Even the most capable models lose accuracy once a task outgrows how much they can hold in mind at once (the context window). We split the work into pieces each model call can manage. Most of the reliability comes from that design work, far more than from the prompt.

  • Control access where agents meet your systems

    Agents call your systems through governed interfaces (MCP servers, API gateways, identity providers), with only the permissions their step needs, instead of operating a screen the way a person would. We design every call to be audited, rate-limited and revocable.

  • Authority per action, people on irreversible ones

    Actions run automatically only within categories and limits you approve in advance. Anything that costs money, cannot be undone or reaches a customer is prepared for a person to approve, unless you have explicitly approved that category for automatic execution, with limits. Anything outside policy, confidence or authority goes to a named person.

  • Free to change models

    The model is a swappable part. Today it might be Claude, GPT or Gemini, and next year a private model on your own infrastructure. We design the process so changing it does not mean rebuilding, and a new model is evaluated against the same criteria before it takes over.

Bring us a process. We will show where an agent fits.

A free 30-minute call with a senior engineer. Bring one problem. We tell you whether it is worth a longer conversation, and if not, who you should talk to instead.

Request a Fit callRead the reality of AI

Agent architectures is one of the streams we watch as part of our forward research.