Episode-XXIV Artificially Intelligent Platform Interface
Authors: Fatih Nar, Shujaur Mufti, Ian Hood, Ather Chaudhry, David Kypuros, Arun Thomas
Remember the last time you called your telecom provider and heard, “Your call is important to us, please hold”? The telecom industry finally realizes that nobody likes to wait — not for calls, data, or innovation. The industry is undergoing a transformative shift, evolving from traditional connectivity providers to sophisticated platform enablers.
With the demand for Network-as-a-Service (NaaS) growing and the rise of standardized network APIs, telcos are poised to redefine the landscape. However, progress has been hindered by networks lacking openness and APIs that are often static and complex, making widespread adoption challenging. But now, with the rise of the Open RAN & API 1st movement, networks are starting to open up, creating new opportunities.
In this next phase, artificial intelligence (AI), open-source technologies, and platform programmability are crucial in taking API capabilities to the next level. Modern application platforms, particularly those with advanced AI-driven interfaces, enable telcos to manage, scale, and optimize network services in real time. By integrating dynamic programmability and automated resource management, these platforms offer telcos enhanced control, flexibility, and scalability, ready to meet the evolving needs of enterprises.
The Role of AI in the API Ecosystem
The Artificially Intelligent Platform Interface (AI-PI) introduces a transformative paradigm for managing APIs. Traditional API frameworks rely on static configurations, manual scaling, and limited automation, which fall short of 5G, edge computing, and IoT drive real-time, mission-critical services. This is where AI-driven orchestration redefines network management.
With AI-enabled capabilities, telcos can automate network configuration, scaling, and management of API-exposed services. Key features include:
- Predictive Scaling: AI-driven insights help telcos predict network traffic spikes and automatically scale network resources to optimize services such as video streaming, gaming, and IoT.
- Real-time Configuration Management: AI-PI can dynamically adjust security policies, resource limits, and traffic routing based on real-time network conditions, enhancing both reliability and user experience.
Commercialization Opportunities
AI-PI holds significant commercial potential, enabling telcos to offer a range of API-driven services for enterprises to seamlessly integrate network functionality. Notable use cases include:
- IoT & Edge Computing: APIs enable low-latency services critical for industries such as healthcare and manufacturing. AI-powered orchestration dynamically allocates resources based on sensor activity, optimizing real-time analytics.
- 5G Network Slicing: Enterprises can request AI-managed network slices on demand, catering to applications like real-time video conferencing and autonomous vehicle communications.
In terms of commercialization, telcos can monetize these services through pay-per-use or subscription models, creating new revenue streams while optimizing operational costs by automating service level adjustments and reducing over-provisioning.
Having AI-PI as an embedded capability, pre-integrated with operational support systems (OSS) and business support systems (BSS), provides powerful, out-of-the-box NaaS functionalities (e.g., Red Hat OpenShift’s AI-PI Agent for 5G Core -> Figure-2 Below).
Enhancing the End-user Experience
Integrating AI-driven orchestration into network APIs significantly improves the end-user experience by intelligently managing East-West (E-W) and North-South (N-S) traffic, optimizing microservices performance, and ensuring seamless load balancing. For users, this means:
- Zero Downtime: Predictive scaling and traffic management ensure smooth application performance even during peak usage.
- Enhanced Security: AI-enabled configuration management allows APIs to automatically adapt security settings based on real-time threat assessments, enhancing protection without impacting performance.
This approach not only boosts service performance but also empowers developers to focus on innovation, accelerating time-to-market for new features.
Value Proposition-Based Examples
- Dynamic Resource Allocation for NaaS: AI-PI allocates network resources based on real-time demand, ensuring optimal service quality during high-demand events without over-provisioning, resulting in cost savings and improved user experience.
- Automated Troubleshooting: Embedded AI monitors network health, identifies issues, and initiates self-healing, escalating to ticketing systems only when necessary, enhancing network reliability.
- On-Demand Network Slicing: Enterprises can request custom network slices for specific applications, with AI-PI managing resources dynamically, unlocking premium service revenue opportunities.
- Adaptive Security Response: AI-PI proactively responds to network threats, adjusting policies and isolating impacted areas, maintaining uninterrupted service, especially valuable to enterprise clients.
- Edge Resource Optimization for IoT: AI-PI dynamically manages edge resources for latency-sensitive applications, ensuring that critical systems remain responsive in smart factories or healthcare facilities.
API to Agent Walk: Embedded Platform AI Agent
The embedded Platform AI Agent adds a transformative layer, enabling intelligent workflows within the platform. Moving beyond static configurations, this AI middleware supports complex task automation through domain-specific services and customizable alerting. The AI Agent streamlines network operations by automating workflows based on real-time data, with limited need for manual oversight.
The modular design (see Figure-4) of this agentic AI infrastructure includes “Agentic Primitive Resources” — foundational building blocks equipped with memory and contextual databases for adaptive decision-making. This architecture enables telcos to create specialized workflows tailored for domains such as telecom, IoT, healthcare, and finance.
Through Southbound API Plugins, telcos can integrate with enterprise-level AI services, large language models, and predictive analytics, enabling the platform to autonomously manage interactions and configurations. This intelligent, middleware-powered approach optimizes performance and resource allocation across various environments, enhancing scalability and resilience in cloud-native operations.
The Role of Hyperscalers
The API ecosystem extends beyond telcos, with hyperscalers playing pivotal roles. Hyperscalers like AWS and Google Cloud enable telcos to optimize multi-cloud and edge environments for API delivery. Similarly, application platform providers like Red Hat Openshift offer APIs to provide abilities for real-time platform programmability. AI-PI empowers these providers to offer intelligent, on-demand scaling of communication services based on real-time demand.
By embracing AI-powered programmability, application platforms enable telcos, hyperscalers, and platform providers to deliver more scalable, intelligent services, driving the next phase of API economy growth.
Looking Ahead: The Future of Network APIs and NaaS
The future of Network APIs lies in AI-driven automation and the evolution of NaaS. With dynamic, intelligent services, telcos can provide:
- Intelligent Network Orchestration: AI will optimize network resource allocation based on real-time data, enabling highly adaptable workloads.
- Advanced E-W/N-S Traffic Control: As applications become more distributed, efficient traffic management will be essential. AI-driven platforms will automate load balancing, traffic prioritization, and resource scaling, supporting seamless operations across cloud environments.
This evolution will enhance network scalability, create new revenue models, and meet the demands of real-time, mission-critical applications.
Conclusion: AI-PI in the API Revolution
As telcos transform their networks into open platforms, AI-driven programmability will become central to API innovation. With AI-PI, telcos can offer advanced orchestration, dynamic scaling, and real-time application management, fundamentally enhancing how network services are delivered and consumed.
In this new era of AI-driven Network APIs, Red Hat OpenShift with Operator Framework is leading the charge, equipping telcos to create smarter, more adaptable networks, shaping the future of digital communications. Through this AI-powered approach, telcos can unlock new revenue streams and provide agile, secure, and scalable services for the coming 6G, distributed edge realizations (finally), and Massive-IoT age.
