Introduction

The rapid rise of artificial intelligence (AI), cloud computing, and data-intensive applications is reshaping how digital infrastructure is built and managed worldwide. As organizations deploy AI-powered services, large language models, multi-cloud environments, and edge computing solutions, the demand for seamless connectivity and intelligent automation has never been greater.

To address these growing requirements, Mplify has announced significant expansions to its automation platform and ecosystem. The initiative focuses on extending automated connectivity across cloud providers, Internet Exchanges (IXs), enterprises, and service providers through its Lifecycle Service Orchestration (LSO) framework. The move is designed to support the next generation of AI-driven networks and cloud infrastructure while reducing complexity and improving interoperability across digital ecosystems.

Formerly known as MEF, Mplify has repositioned itself as a global alliance focused on accelerating the AI-powered digital economy through standardization, automation, certification, and collaboration.

This article explores Mplify’s latest automation initiatives, the technologies behind them, and what they mean for the future of AI-ready infrastructure.

The Growing Demand for AI and Cloud Infrastructure

The global technology industry is experiencing unprecedented growth in AI adoption.

Organizations are increasingly deploying:

• Generative AI applications
• Machine learning platforms
• AI agents
• Multi-cloud architectures
• Edge computing environments
• Data-intensive business applications

These technologies require highly interconnected infrastructure capable of processing and transporting massive amounts of data efficiently.

Traditional networking approaches often struggle to keep pace with modern AI workloads because they rely heavily on manual configuration, fragmented management systems, and limited interoperability between providers.

As AI applications become more distributed across cloud platforms, data centers, and enterprise environments, automation becomes essential.

This is where Mplify’s expanded automation framework enters the picture.

What Is Mplify?

Mplify is a global alliance of network operators, cloud providers, cybersecurity companies, enterprises, technology vendors, and infrastructure providers working together to accelerate digital transformation.

The organization evolved from MEF, a long-established industry body known for developing Carrier Ethernet standards and networking certifications. In 2025, MEF rebranded as Mplify to reflect its broader focus on AI, automation, cloud infrastructure, cybersecurity, and Network-as-a-Service (NaaS).

Today, Mplify’s mission centers on creating standardized frameworks that allow organizations to automate and scale digital services across interconnected ecosystems.

Key focus areas include:

• Network automation
• Cloud connectivity
• AI-ready infrastructure
• Service orchestration
• Cybersecurity integration
• Network-as-a-Service (NaaS)

Expanding the Automation Ecosystem

One of Mplify’s most significant recent announcements is the expansion of its ecosystem to include major Internet Exchange providers.

Organizations including:

• AMS-IX
• DE-CIX
• LINX

have joined Mplify to collaborate on the evolution and implementation of Lifecycle Service Orchestration APIs. These APIs support automated IP peering, cloud connectivity, and emerging AI-driven traffic exchange services.

The inclusion of major Internet Exchanges significantly expands the reach of Mplify’s automation framework across global digital infrastructure.

Internet Exchanges play a critical role in connecting:

• Internet service providers
• Cloud providers
• Enterprises
• Content delivery networks
• Digital platforms

By bringing these organizations into a common automation framework, Mplify aims to create a more seamless and interoperable ecosystem.

Understanding Lifecycle Service Orchestration (LSO)

At the center of Mplify’s strategy is Lifecycle Service Orchestration (LSO).

LSO is a standardized API framework designed to automate the entire lifecycle of digital services.

Rather than relying on manual processes, organizations can use LSO APIs to:

• Order services
• Configure networks
• Provision connectivity
• Monitor performance
• Manage faults
• Automate billing
• Coordinate multi-provider environments

The goal is to create a programmable networking environment where services can be delivered and managed automatically across multiple organizations and infrastructure providers.

This becomes increasingly important as AI applications require dynamic connectivity between cloud platforms, data centers, and enterprise systems.

AI-Native Automation Through the Kylie SDK

Earlier in 2026, Mplify announced the release of its Kylie SDK, a major upgrade to its automation platform.

One of the most notable features of the release is support for the Model Context Protocol (MCP), which enables AI agents and large language models to interact directly with networking infrastructure through standardized APIs.

The Kylie release introduces:

1. AI-Native LSO APIs

The platform now includes full MCP support across its API portfolio, allowing AI systems to participate in orchestration and operational processes.

2. Enhanced Business APIs

Updates to business-focused APIs help automate functions such as:

• Quoting
• Ordering
• Partner coordination
• Billing

3. Operational Automation

Enhancements improve:

• Service provisioning
• Network monitoring
• Fault management
• Performance assurance

These capabilities support the growing demand for intelligent, automated infrastructure management.

Why AI Infrastructure Requires Automation

AI systems operate differently from traditional applications.

Modern AI workloads often require:

• Large-scale compute resources
• High-performance networking
• Multi-cloud deployments
• Real-time data movement
• Dynamic scaling

Managing these environments manually is increasingly impractical.

Automation helps organizations:

1. Reduce Complexity

AI environments frequently span multiple providers and infrastructure domains. Automated orchestration simplifies coordination across these systems.

2. Improve Speed

Provisioning services through APIs can occur in minutes rather than days or weeks.

3. Increase Reliability

Standardized automation reduces human error and improves consistency.

4. Support Scalability

As AI workloads grow, automation enables infrastructure to scale efficiently.

These benefits are becoming essential for organizations deploying AI at enterprise scale.

Advancing Network-as-a-Service (NaaS)

Mplify views automation as a foundational element of Network-as-a-Service.

NaaS enables organizations to consume networking capabilities in a manner similar to cloud services, on demand, programmable, and scalable.

According to Mplify, modern NaaS solutions combine:

• Connectivity
• Multi-cloud networking
• Security
• Application assurance
• Automated orchestration

within a common standards-based framework.

The organization’s automation initiatives are designed to accelerate adoption of NaaS by making network services easier to deploy and manage.

Supporting AI-Driven Traffic Exchange

AI is creating new networking requirements that extend beyond traditional internet traffic patterns.

Large AI models require:

• Massive data transfers
• High-bandwidth connectivity
• Low-latency communications
• Distributed compute coordination

Mplify’s collaboration with Internet Exchanges specifically addresses emerging AI-driven traffic exchange requirements.

By extending automation into interconnection ecosystems, providers can support more efficient movement of AI workloads across networks, cloud platforms, and data centers.

This capability is expected to become increasingly important as AI adoption continues to grow.

Industry-Wide Collaboration

One of Mplify’s distinguishing characteristics is its emphasis on ecosystem collaboration.

Its membership includes organizations across multiple sectors, including:

• Telecommunications
• Cloud computing
• Data centers
• Cybersecurity
• Enterprise technology

Recent additions such as Graphiant demonstrate growing industry interest in standardized approaches to automation and AI-ready networking.

The alliance model enables stakeholders to collaborate on common standards rather than building isolated solutions.

Building Infrastructure for the AI Economy

Mplify has consistently positioned its initiatives around supporting what it calls the AI-powered digital economy.

This vision recognizes that future digital services will require infrastructure that is:

• Automated
• Programmable
• Secure
• Interoperable
• Scalable
• AI-ready

The organization has also expanded certification programs focused on validating infrastructure capable of supporting AI and agentic AI workloads across multi-provider environments.

These efforts aim to provide enterprises with greater confidence when selecting infrastructure providers.

Challenges Ahead

While automation offers significant benefits, adoption is not without challenges.

Organizations must address:

1. Integration Complexity

Many enterprises operate legacy systems that may require modernization before automation can be fully implemented.

2. Standards Adoption

Success depends on widespread industry participation and adherence to common frameworks.

3. Security Requirements

Automated environments must maintain strong cybersecurity protections.

4. Skills Development

Organizations need professionals capable of managing increasingly automated infrastructures.

Addressing these challenges will be critical to realizing the full potential of AI-driven automation.

Future Outlook

The future of digital infrastructure is likely to become increasingly automated and AI-enabled.

Mplify has already outlined plans for future platform enhancements, including additional AI-driven capabilities through its upcoming Lana release scheduled for later in 2026.

As enterprises continue investing in AI, cloud services, and digital transformation, demand for automated infrastructure frameworks is expected to grow significantly.

Organizations that can deliver interoperable, standards-based automation may play a central role in shaping the next generation of digital services.

Conclusion

Mplify’s expansion of its automation platform reflects a broader industry shift toward AI-ready, cloud-connected, and highly automated digital infrastructure. By extending its Lifecycle Service Orchestration framework to include Internet Exchanges, enhancing AI-native APIs through the Kylie SDK, and promoting Network-as-a-Service adoption, the organization is helping create the foundation for future digital ecosystems.

As AI workloads become more complex and distributed, automation will increasingly serve as a critical enabler of scalability, efficiency, and interoperability. Through collaboration, standardization, and innovation, Mplify is positioning itself at the forefront of this transformation.

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