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AI agents for operations that clear the backlog

Agents for the work that keeps operations running

We build agents that process orders, documents and exceptions across your systems, with a person approving the steps you choose.

Book a free consultation How we build agents
ops-desk

  1. Operations leadYour team

    A supplier pushed back a delivery. Which open orders does it hit, and can we tell the customers today?

  2. Order agentAgent

    1. erp.list_open_orders orders waiting on that part
    2. supplier.get_eta new delivery date
    3. schedule.replan ship dates by priority

    The affected orders have new ship dates. Two key accounts slip past their promised date, so they need a sign-off.

    Needs a personShip dates past the promiseNew dates for two key accounts
    Approved by Operations lead

  3. Update agentAgent

    1. email.draft_updates one note per customer
    2. erp.update_orders new date and the reason

    Updates sent from the shared mailbox. Every order now carries its new date and the reason for the change.

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

Operations run on handoffs, and handoffs wait.

Agents take the routine steps and hand back the ones that need a person.

  1. 01

    Where it stallsRoutine requests queue behind a few busy people.

    What we do Agents read each order, form or ticket as it arrives and complete the standard steps in your systems.

  2. 02

    Where it stallsData gets retyped from one system into the next.

    What we do An agent reads documents and emails once and writes the fields to your ERP, CRM or ticket queue.

  3. 03

    Where it stallsExceptions surface late, after a customer notices.

    What we do Agents check every case against your rules and raise exceptions to their owner with the context attached.

What we build

What we build for operations teams

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

Operations work we take on

  • Order handling
  • Customer requests
  • Document data entry
  • Scheduling
  • Inventory questions
  • Supplier follow-up
  • Exception handling
  • Status reporting

How we build it

  • Order and request handling

    Agents take orders and requests from email or forms, check them and enter them in your ERP.

  • Document reading and data entry

    Forms, delivery notes and scans read into structured fields, each linked to its source line.

  • ERP, ticketing and inbox connections

    Read and write access to the systems your team runs on, with the permissions you set.

  • Process rules and approval steps

    We map where each step waits and agree with your team which ones stay with a person.

Not seeing your case? Bring the process with the most manual steps 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 operations

What are AI agents for operations? +
AI agents for operations are software agents that take routine operational work from start to finish. They read orders, forms and tickets, check them against your rules, update your ERP or ticket queue and pass exceptions to the right person. Each agent works only through the systems and permissions you give it. You decide which steps wait for a person, and every action is logged.
Which operations workflows should we automate with AI agents first? +
Start with work that arrives in volume, follows your rules most of the time and waits on a few people: order entry, device or account activation, document intake and exception monitoring. We map the workflow with your operations lead, pick one step and agree a success threshold on your own past cases. Once it passes, the same pattern moves to the next workflow.
What have you built for operations teams? +
For a US telecom, 98% of device activation workflows are automated and error processing is 10x more efficient. For a US health insurer, claim processing fell from 3 hours to 8 minutes, with a specialist still deciding. Mixam's agent handles over 96% of web-to-print orders end to end. We have no published case for supplier delays and delivery planning yet.
How do AI agents connect to our ERP and other systems? +
Through the APIs and connectors your systems already offer, with a separate set of permissions for each agent. Business rules such as order limits and promised dates stay in tested code, so they always give the same answer. Actions that commit you to a customer wait for the person you name. Every step is logged with its inputs, so your team can trace any change.
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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