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the vision
Agentic OMS evolves order management from recording to acting
Order management has always recorded and routed by static rules. We make it act. The Agentic OMS connects your network — inventory, orders, fulfillment — and puts named agents to work on the decisions that matter, scoped and human-led.
Every order kept, on time and in full.
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The shift
Every order is a decision, and decisions have outgrown the rules
Commerce has become more dynamic, more distributed and more complex. A modern fulfillment network makes thousands of decisions every hour: where to source, how to route, which carrier, when to intervene. Static rules answer those questions the same way every time, whether or not the situation has changed. That worked when networks were simple. It breaks under the pace, the volume and the variability of what retail actually looks like now.
The answer is not more rules. It is a system that understands the goal behind every order and acts on it.
The Category
A system that understands goals, not just rules
An Agentic OMS does three things a traditional OMS cannot. The discipline behind it: copilots advise, agents act on their own processes, and deterministic logic keeps doing what it does best.
First
Understanding
It understands the goal: on time, at the right cost, within certain constraints. It is not just a static rule to execute.
Second
Evaluating
It evaluates the live context, inventory, capacity, cost and exceptions, at the moment the decision is needed.
Third
Acting
It acts within the scope you define, learns from what happens next, and gets better with every order.
WHO DOES WHAT
A copilot that advises, agents that act
fulfillmenttools Copilot
The fulfillmenttools Copilot is the single interface to fulfillmenttools AI. It surfaces fulfillment insights and inventory answers in plain language, so your teams decide faster.
Oder Routing Agent
The Order Routing Agent turns the retailer's goals, cost, speed, satisfaction and capacity, into real-time sourcing decisions, and can explain every one.
Order Monitoring Agent
The Order Monitoring Agent watches every order end to end and resolves risk to the promise before your customer feels it. Your client advocate for on time in full.
Top rated & recognized as industry leader
What's it for
Every order delivered,
on time and in full
The point of an Agentic OMS is not intelligence for its own sake. It is the promise you make to your customer, kept at scale: lower cost to serve on every order, protected margin, better use of the capacity you already have, and fewer markdowns because inventory sits where demand is. Your teams spend their time on decisions that matter, and your customers notice the difference in every delivery.
HOW YOU GET THERE
Built for the agentic future,
on your terms
You don't have to choose between staying in control and staying open. fulfillmenttools lets you set the pace of autonomy internally, while making sure any agent — yours or not — can plug into accurate fulfillment data.
Autonomy grows with trust
No system should shift to full autonomy overnight. The Agentic OMS matures with you — understanding your operations, configuring logic to your goals, continuously optimizing decisions, and executing within the scope you set. You control the pace; human oversight scales with your maturity.
The Vision
Where this is heading
Our north star is an OMS where your teams, our agents and the agents of your ecosystem work together on one goal: every order moving through a self-optimising fulfillment network, scoped, governed and human-led at every step. That is what order management looks like when it stops recording and starts acting.
Frequently asked questions
Everything you need to know about the evolution of Agentic Order Management as well as how our agents work and connect with your platform.
All topics
Agentic AI
Built-in AI
Connect AI
What are the evolution stages of an order management system?
Order management has evolved through four stages: a classical OMS handling orders in a single centralized system, a distributed order management system coordinating across multiple systems and channels, an AI driven OMS using AI and machine learning to optimize and predict, and an Agentic OMS where autonomous agents monitor, decide and act across the ecosystem.
Does an Agentic OMS replace human decision-making?
No. An Agentic OMS takes over decisions within a scope your team defines, and humans stay in charge of everything outside it. In fulfillmenttools, agents notify by default, ask for approval above a confidence threshold reads backwards. Approval is usually needed below a confidence threshold, above a risk or value threshold.
What are the internal AI agents and what do they do?
fulfillmenttools offers one copilot and two acting agents. The AI Sidekick is the single entry point to fulfillmenttools AI, surfacing fulfilment insights and inventory answers in plain language so teams decide faster. The Order Routing Agent turns goals into real-time sourcing decisions and can explain every one. The Order Monitoring Agent watches every order end to end and resolves risk within scope. Both act scoped and human-led: they notify by default, ask for approval above a threshold, and co-decide on systemic issues.
Which data can external AI agents access?
Through the fulfillmenttools MCP Server, external AI agents can access real-time inventory availability, product data, delivery promises, fulfillment options, and live order data, as well as facilities, pick and pack jobs, shipments, returns, and routing decisions. Access is limited to what the authenticated user is allowed to see, and the current release is read-only.
Is AI agent access to fulfillment data secure?
Yes. Every request to the fulfillmenttools MCP Server uses OAuth 2.1 with PKCE, and the agent always requests data in the name of an authenticated user, never on its own. Access is permission-scoped to what that user is allowed to see, the current release is read-only, and data stays hosted on Google Cloud infrastructure in Europe.
When do agent actions need human approval?
You define it. By default, fulfillmenttools agents notify your team about actions within their scope, ask for approval above a set confidence threshold and co-decide with a human on systemic issues. Terminal actions, which can't easily be undone, require higher confidence or explicit human confirmation.