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Your own MCP server for agent to agent ordering

Let your customers' AI assistants order from you

For Rendreal we built an MCP server that lets compatible AI assistants find a print product, prepare artwork and create an order for their user.

Book a free consultation How we build agents
agent-orders

  1. Customer assistantAgent

    1. mcp.list_tools products, design, quote, order

    My user needs folded leaflets for an event, with their logo. Which products and options do you offer?

  2. Order agentAgent

    1. catalogue.match folded leaflet options
    2. design.create artwork from the user's logo
    3. config.validate size, paper and fold checked

    Artwork and a quote are ready. One fold is not available, so I offered the closest option.

  3. Order agentAgent

    1. order.create order with a checkout link

    The order is created. Payment waits for the user on the secure checkout page.

    Needs a personPay for the print orderSecure checkout page, outside the chat
    Approved by Customer

  4. Order agentAgent

    1. supplier.send_job print and delivery

    Payment confirmed. The job went to the print supplier set up for this product.

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

Your buyers' AI assistants cannot order from you yet.

An MCP server opens your products and rules to their assistants, and the customer still pays.

  1. 01

    Where it stallsBuyers ask an AI assistant first, and it cannot order.

    What we do An MCP server turns your catalogue, quotes and orders into tools that compatible assistants can call.

  2. 02

    Where it stallsOutside agents ask for options you do not offer.

    What we do Each request is checked in code against your real options. Unsupported ones get a clear answer.

  3. 03

    Where it stallsNobody wants an outside agent spending money.

    What we do The agent prepares the order. The customer pays on your own checkout page, outside the chat.

What we build

What we build for agent orders

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

Orders that come in from

  • Consumer AI assistants
  • Customers' procurement agents
  • Partner and reseller agents
  • Marketplace agents
  • Reorders and repeat orders
  • Quote requests
  • Order status checks
  • Returns and changes

How we build it

  • An MCP server for your services

    Your catalogue, quotes and orders published as tools that compatible AI assistants can find and call.

  • Specialist agents behind the tools

    Some tools hand the work to a specialist agent, for example product configuration or design.

  • Product checks in code

    Every request is checked against your real product options before a quote or an order is made.

  • Access and payment rules

    You decide what outside agents may read and do, and every payment stays on your checkout page.

Not seeing your case? Tell us which agents your customers use and what they order, and we start from there.

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.

Where it runs

Teams and industries that use it

Each page shows where agents fit in that team or industry.

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

Agent to agent ordering, answered

What does agent to agent mean for a business? +
Agent to agent means one AI agent working with another on a person's behalf. For a business, it usually means a customer's AI assistant uses your services directly: it finds a product, gets a quote and prepares an order inside the conversation. Today the common way to offer this is an MCP server, which lets compatible assistants discover and call your tools.
Which services suit agent to agent ordering first? +
Products with clear options and rules, such as printed materials, configurable parts, bookings or repeat orders. An outside assistant chooses well only when your catalogue, prices and limits exist as data it can read. Start with one product line, publish quoting and order creation as tools and keep payment on your own checkout page. Sales that need a long conversation stay with your team.
Have you built agent to agent ordering before? +
Yes. For Rendreal we built an MCP server that lets compatible AI assistants find print products, prepare artwork, get a quote and create an order with a checkout link. Some of its tools hand work to specialist agents for product configuration and design. It connects assistants to Rendreal's own integrated services and suppliers. It does not show independent supplier agents finding and negotiating with each other.
How do you stop an outside agent from placing a wrong order? +
The outside agent reaches only the tools you publish, and each tool checks its input in code. Product options are validated against your catalogue, and a request the product cannot support gets a clear answer instead of a guess. The agent can create an order, but the customer confirms payment on your checkout page. Every tool call is logged with its input and result.
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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