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Built for the Agentic Enterprise
The platform the agentic enterprise runs on
One governed foundation. Every person, every agent, every process.
Is your stack optimized for the agentic enterprise?
More AI investment often leads to more disconnected tools. The stack keeps growing. The gaps stay open.
Mendix closes those gaps.
- Enterprise context connected across every system
- AI output is grounded in your operational context
- Solutions, agents, and people working together in governed worklows
- Single signals coordinating actions downstream
- Every agent decision is traceable, auditable, and in policy
Closing those gaps does not require replacing what you have built. Mendix runs on top of the infrastructure, data, and tools you already have. It connects them, coordinates them, and enforces governance across all of it.
Trusted By
The agentic enterprise requires five things:
Context
Build shared enterprise understanding across every system: ERP, CRM, OT, and beyond. Agents and humans need the same version of reality.
Explore Enterprise Knowledge GraphIntelligence
Deliver AI grounded in your operations. Generic models don't know your enterprise. Custom models produce outputs people and agents can act on.
Explore AI Model DevelopmentCoordination
Design solutions and agents on a single foundation. The right agent acts. The right human steps in with the context they need.
Explore Agentic DevelopmentAlignment
A signal anywhere in the enterprise triggers coordinated action across every part of the enterprise as one traceable end-to-end process.
Explore Process OrchestrationTrust
Built in from day one instead of layered on later. Every action is traceable. Every decision is auditable. Be ready before anyone asks.
Explore Enterprise AI GovernanceThere’s only one platform that enables you to close all five gaps.
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We are seeing a trend in the market where AI agents are going to play a significant role in automating workflows or helping collaboration between users. I always felt Mendix was at the cutting-edge of the technology changes that are happening, and integrating AI agents into workflows is one area where I feel that we can collaborate with Mendix.
Venky Yerrapotu Co-Founder and CEO, 4CRisk
Five solutions. One platform.
Mendix is built around five integrated capabilities. Each one closes a gap between AI investment and business return.
Context
Every enterprise runs on data spread across dozens of systems. No single system sees the whole picture. This is exactly where agents fail.
Graph Studio builds an enterprise knowledge graph across every system you already run without moving, copying, or replicating a single record. It maps not just what your data contains, but how every entity, relationship, and domain connects to every other, across your full enterprise ontology.
That picture is what makes agents reliable. Every model and solution on the platform reasons from the same context, traces every answer back to its source, and acts without rediscovering what your enterprise already knows. Domain expertise gets encoded into the graph itself, not locked inside the people who understand it, so institutional knowledge scales with your workforce, not against it. Auto-generated ontologies and composable graphmarts mean no months of upfront modeling, and no rebuilding what already works when you add new domains. Teams are up and running in weeks, not years.
Intelligence
Generic AI models are not built for the complexity of your unique workforce and processes. The jobs that matter most in operations, like predictive maintenance, anomaly detection, root cause analysis, and risk scoring, require models trained on your data, grounded in your context, and built to reach production and stay there.
Building and deploying those models requires closing the execution gap: getting from development to production without losing the business knowledge, transparency, or governance that make AI decisions actionable. Domain experts and data scientists work together across the full model lifecycle on the same platform, from prototype through deployment, so the people who understand the problem stay involved through delivery.
Models deploy as governed API endpoints, accessible to any agent workflow on the platform. Integrated drift monitoring and continuous performance tracking mean models stay accurate over time, not just on launch day.
Coordination
AI projects fail to deliver value or remain stuck in the pilot stage because they’re simply not production-ready. The bottleneck shifts downstream, and the cost of fixing it grows.
With Mendix, teams build and deploy traditional web and mobile solutions as well alongside AI agents and agentic solutions that handle real operational complexity from day one.
Maia, the agentic AI built into the platform, plans and builds on your organization’s real context, from its architecture, guidelines, standards, and approved components. Maia Make executes multi-step development autonomously, from requirements to working software. AI generates into visual models, not raw code, so anyone who understands the problem can contribute to the solution without specialist skills at every stage.
The result is working software delivered faster, with less back-and-forth and less rework. From a single use case to the full enterprise portfolio, on the same platform from day one.
Alignment
An RPA tool here. An iPaaS platform there. A workflow tool for each department. The average large enterprise now runs automation across dozens of disconnected systems. Add AI agents on top of that fragmentation and the result is processes no one fully owns and agents no one fully governs.
The platform’s process orchestration capabilities solves that. A supplier alert, a risk threshold breach, a case falling outside its SLA. Each one should trigger coordinated action across every function it touches. With Mendix, you create a single layer that connects business events, AI signals, and human decisions across the entire enterprise, coordinating agents, systems, and people simultaneously as one governed process.
Every step visible. Every handoff accountable. Every action traceable from trigger to outcome.
Trust
Most enterprises govern by exception. When running an agentic enterprise, that model does not scale. It’s fertile ground for shadow AI, growing compliance backlogs, and stalled out production lines.
Within the platform, governance starts from the point of creation. Enforce policies centrally across every app, agent, and workflow, not per team, not per project. Everywhere. Every model inference, agent decision, human approval, and data lineage event is traceable, logged, and auditable before anyone asks for it.
As the portfolio grows, governance absorbs the complexity rather than creating it. New apps and agents inherit the same enforcement layer automatically. You get built-in audit trails, explainability, and policy controls.
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Even when AI works technically, companies fail to capture value if they do not redesign workflows, roles, and governance for humans and AI to work together.
MIT BCG
What the agentic enterprise looks like
Datascalehr is a platform-as-a-service (PaaS) that uses AI and ML to connect any data source to any payroll and reconcile them. They adopted Mendix to launch its AI-native platform and transform payroll software.
Their platform’s model solves a decade-long challenge of multi-country payroll operations by establishing the right connectivity to their client’s data and payroll partner network, eliminating the need for code maintenance and reconciliations.
Features include real-time tracking and validation, data synchronization across ecosystems, and continuous monitoring, enabling its customers to focus more on problem solving and less on technical execution.
Understand the agentic enterprise
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Frequently Asked Questions
We have already invested heavily in AI. Why are we not seeing results?
The bottleneck is rarely due to the model. It is the absence of enterprise context: the semantic relationships, business logic, and cross-system meaning that tell an agent what actually matters in your organization. Data lakes and LLM integrations do not provide that.
Most organizations expect AI ROI in seven to twelve months. Actual return, if it materializes at all, takes two to four years, and only six percent reported payback in under a year (Deloitte). It’s necessary to find a platform that addresses the root cause by building a live knowledge graph that connects every enterprise system into a unified ontology agents and people can reason across, without replacing what you have already built.
What is missing from our current AI stack?
Up to five things, in most cases. 1. Enterprise context that gives agents a shared understanding of how the organization actually works. 2. Intelligence grounded in your operational data, not generic models. 3. Coordination that brings people and AI agents together by design, not as an afterthought. 4. Orchestration that connects action across every function simultaneously as one traceable process. 5. Governance enforced at the point of creation rather than added afterward. Most enterprises have invested in one or two of these. The platform provides all five in a single architecture.
Does the platform require us to replace our data platform?
No. The knowledge graph is built on top of your existing data estate, adding the semantic context that data lakes and cloud platforms do not provide on their own. No migration required, no source systems replaced.
How does the platform work alongside our existing infrastructure?
It connects to existing systems—ERP, CRM, OT, financial, supply chain, for instance—without , replacing them. The knowledge graph sits on top of your current estate. You do not need to rebuild your stack or move your data to deploy at scale.
Does the platform require us to hire data scientists or grow the engineering team?
The low-code environment lets business and IT teams collaborate to deliver production-ready applications and AI agents without data science expertise or additional headcount. As an example, Brazilian glass manufacturer, Vivix, deployed 17 production applications in one year using an existing team, no new data science hires.
How does the platform handle AI governance in regulated industries?
The platform’s architecture is designed to hold up under regulatory scrutiny in highly regulated industries like financial services, healthcare, manufacturing, and the public sector.
How does the platform address shadow AI?
It’s right to be concerned about Shadow AI. 82% of CIOs say employees are building AI agents faster than IT can govern them. Mendix gives IT the governed infrastructure that business teams are currently working around. When it is fast enough and accessible enough for business and IT to build together, the incentive to go outside governance disappears.
Mendix gives you centralized policy enforcement, full auditability, and a low-code environment that does not require specialist expertise to close the gap between what IT can govern and what the business wants to build.