Pillar

AI Agents & Automation

We design AI agents that handle repetitive IT and business tasks, with scoped permissions, metrics and a named person accountable for anything critical.

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When does it make sense?

  • Your team spends the day on repetitive tickets.
  • Processes that depend on copying data between systems.
  • You want to use AI without losing control of what it does.

Scope

What’s included

  1. Prioritized use cases

    We pick tasks with volume, clear rules and a low cost of error.

  2. Agents for IT operations

    Ticket triage, first-line diagnosis and answers to frequent requests.

  3. Process automation

    Integration with your systems through APIs, with every action logged.

  4. AI governance

    Minimal permissions, control over the data the agent uses, and human review of anything critical.

  5. Metrics

    How many tasks it resolved, how many it escalated and how much time it freed up for your team.

Method

How we work

Three steps, always with the same team and everything documented.

  1. 01

    Use case

    Task, data, risks and who supervises.

  2. 02

    Measured pilot

    The agent works under supervision while we measure results.

  3. 03

    Operations

    We expand what worked and adjust with your metrics.

Reference architecture

An agent that acts within permissions and answers for it.

The agent proposes and executes what is allowed; anything critical goes through a person.

  1. 01Input

    • Tickets and requestsemail · portal · chat

  2. 02Agent

    • AI agenttriages · diagnoses · proposes

  3. 03Tools

    • Allowed actionsleast-privilege APIs
    • Knowledge basedocumented procedures

  4. 04Control

    • Human reviewapproves anything critical

  5. 05Record

    • Ticketing systemevery action is logged
Reference example. The final design comes out of the review of your environment. AWS and Azure products are named as text only. Swipe to see every layer.

FAQ

What people usually ask us

Does the agent make decisions on its own?

Only on the tasks you define as low risk. Anything critical is always approved by a person on your team.

Which models do you use?

Language models available on Azure and AWS, chosen by cost, quality and where your data is processed.

How do I know it works?

We define the metrics before the pilot: tasks resolved, escalated, errors and time freed up. If they don’t improve, we tell you.