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AI agents for IT teams that clear the ticket queue

Agents that take the routine tickets off your team

We build agents that triage tickets, handle access requests and answer data questions, with an engineer approving any change to live systems.

Book a free consultation How we build agents
it-helpdesk

  1. Service desk leadYour team

    A new hire starts on Monday and still has no accounts or laptop. Can an agent sort it out?

  2. Access agentAgent

    1. hr.get_new_hire role, team and start date
    2. idp.create_account sign-in and default groups
    3. tickets.update status for the requester

    Accounts are ready. The role also asks for admin rights on the finance system, which need the owner's approval.

    Needs a personAdmin rights on a finance systemRequested for a new hire's role
    Approved by System owner

  3. Device agentAgent

    1. mdm.assign_device laptop and standard apps

    Laptop enrolled with the standard apps. The ticket is closed and the sign-in details went to the manager.

Example data. An agent at work in a team channel, with a person approving the step you choose.
Mixam: conversion to a final sale from about 20% to about 40%
Where agents help

IT teams lose their week to repeat tickets.

Agents handle the routine requests and bring in an engineer for admin rights and changes to live systems.

  1. 01

    Where it stallsRepeat requests bury the work that needs an engineer.

    What we do Agents resolve access, password and setup requests through your directory and close the ticket.

  2. 02

    Where it stallsTickets bounce between queues before anyone acts.

    What we do An agent reads each ticket, adds the user and device history and routes it to the right team.

  3. 03

    Where it stallsAlerts arrive faster than anyone can read them.

    What we do Agents group related alerts, pull recent changes and logs, and hand on-call a short summary.

What we build

What we build for IT teams

Whatever comes in, every project is built from the same parts below, set up for your files and your systems.

IT work we take on

  • Service desk tickets
  • Access requests
  • Onboarding and offboarding
  • Password and account issues
  • Incident reports
  • Data questions
  • Release testing
  • Monitoring alerts

How we build it

  • Service desk and ticket triage

    Agents read, enrich and route tickets, and resolve the routine ones through your own tools.

  • Access and onboarding requests

    Accounts, groups and devices set up from HR data, with admin rights waiting for an owner.

  • Data questions over your databases

    A text-to-SQL agent answers questions in plain language and shows the query behind each answer.

  • Testing before release, tracing after

    Each agent tested on your past tickets before release and traced step by step once live.

Not seeing your case? Bring the request that fills your queue and we start from that.

Tell us about your workflow
How we build agents

Agents that work as part of your team

They work in the tools your team already uses, take on whole tasks and stop for a person where you decide.

  1. Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
  2. Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
  3. Approvals log: each action that waited for a person, with who asked, who decided and when.
Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
Approvals log: each action that waited for a person, with who asked, who decided and when.

Screens from the workspace our agents run in, with example data.

Industries

Industries we build agents for

Each page shows where agents fit in that industry and what we have built there.

All industries
Engineering in the open

We help build what agentic AI runs on

Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.

Shaping agentic AI

Agentic AI Foundation

Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.

Read the announcement

Open source

We build open-source agent tooling that other engineering teams install

10M+ PyPI downloads
4,000+ GitHub stars
33 public repositories

Framework contributors

When the framework breaks, we fix it at the source

We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.

Research

Our engineers and PhDs publish peer-reviewed research

Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.

Track record

Frontier AI teams now build on what we shipped first

Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.

Case studies
All case studies
Engagement models

Ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Start with one workflow

We map one workflow with your experts and agree a success threshold before any build.

Best whenYou want the success test agreed before you commit to a build.

Book a free consultation
  1. Process redesign and build Best whenYou want one team accountable for the outcome.
FAQ

Questions about AI agents for IT teams

What are AI agents for IT teams? +
AI agents for IT teams are software agents that work the IT queue alongside your engineers. They read tickets, add user and device history, resolve routine access and setup requests, summarise alerts for on-call and answer data questions from your databases. Each agent uses only the tools and permissions you give it, and changes to live systems wait for an engineer.
Which IT tasks can AI agents automate first? +
Start with requests that repeat every week and follow a known runbook: password resets, access requests, new hire setup, ticket routing and first-line answers from your internal docs. Pick one queue, agree a resolution threshold with your service desk lead and test the agent on last month's tickets. Incident summaries and data requests usually follow once the first queue runs well.
What have you built for IT teams? +
For a US manufacturer we built a text-to-SQL agent that answers questions over 473+ columns across 15 database views. For Synera, creating a validated engineering workflow went from up to an hour to about 2 minutes. For a US telecom, an error-ticket monitoring agent made error processing 10x more efficient. We have no published case for access provisioning or incident response yet.
Can an AI agent make changes to live systems? +
Only through the actions you allow, and most teams start with read access. Each agent gets its own scoped credentials, so it can reach only the systems you connect. Changes to live systems, admin rights and anything irreversible wait for a named engineer to approve. Instructions found inside tickets or emails are treated as data, and every call the agent makes is logged.
How do you know it works before the full build? +
We agree a success threshold with your team before the build and test the agent on your own cases in a Proof of Value. It measures accuracy and running costs, and the build goes ahead only when the threshold is met. Corrections from your team go back into the test set, so every later version is checked against them.
Who owns the system, and which models does it run on? +
You own the code, prompts, evaluation sets and rules by contract. The agents run in your environment on hosted or open-weight models. The model sits behind an interface in code, so you can change it when accuracy, speed or your hosting rules change, without rebuilding the agent.
Start with one workflow

Test one workflow on your own data

A Proof of Value shows accuracy and running costs before the full build.

Book a free consultationRead case studies
From the blog

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