We’ve reached the final stage on the journey toward the Self-Driving Network: a fully autonomous system that configures, troubleshoots, and optimizes the client-to-cloud user experience with minimal human intervention. What was once a distant aspiration is now becoming a tangible reality.
Throughout this series, we’ve explored the foundational stages that pave the way to an autonomous Self-Driving Network:
- Stage 1 – Data: Getting the right high-quality, real-time telemetry data to the cloud.
- Stage 2 – Insights: Processing and translating that data into meaningful context for every user connected to the network.
- Stage 3 – Recommendations: AI/ML and advanced algorithms quantify the contribution of network features to poor user experience minutes and suggest recommendations for swift remediation.
- Stage 4 – Assisted: AI-generated actionable and automated recommendations with evidence that IT can either act upon or, through earned trust, give Marvis® AI Assistant permission to act.
Now, in Stage 5, we enter the self-driving era. This is where IT has gained trust in AI and given it permission to identify and resolve issues independently while staying informed on all actions taken.
Building a Self-Driving Network and trust
Getting to a Self-Driving Network doesn’t happen overnight. For vendors, it takes years of developing scalable and secure cloud-based infrastructure and training AI models on the right data. For IT teams, it takes time to overcome initial AIOps skepticism and build confidence and trust in the AI’s accuracy and value. As AI demonstrates reliability and transparency, IT can delegate more tasks, thus reducing manual overhead and freeing up time for work that’s more strategic, creative, and transformative. This shift enables IT teams to lead innovation efforts, support business growth, and evolve their roles beyond routine maintenance.
Leading the journey to a Self-Driving Network with agentic AI
We’ve been working toward the vision of a Self-Driving Network for over a decade—developing the Mist™ AI-native networking platform with more advanced AI capabilities while continuously improving the accuracy and efficacy of AI outcomes over time. Our previous post explored how Marvis Actions already performs some self-driving actions—with IT’s approval.
This capability marks the beginning of autonomy, where the network can perceive, reason, and act independently. Now, we’re building on that progress with the integration of agentic AI, where intelligent agents can reason, plan, collaborate, and execute multistep tasks across complex environments. Agentic AI acts as a catalyst, accelerating the transition from “assisted-driving” to “self-driving.”
However, while agentic AI adds another powerful capability to AI for network operations, to be truly successful, there must first be a strong foundation. Here, that strong foundation starts with our Marvis AI Assistant and its conversational interface, Marvis Minis, Marvis LEM, and Marvis Actions. Agentic AI is the next step in adding to these powerful capabilities, further enabling Marvis to operate with greater context and precision to achieve a fully Self-Driving Network, all while keeping humans informed and in control.
Marvis Minis and Marvis LEM deliver proactive and predictive insights
Marvis Minis is a digital experience twin that simulates user behavior to proactively test critical network services and critical business applications. Minis can detect issues before a business opens its doors and correct those detected issues before they impact real users. With recent enhancements, Minis now analyzes experiences end to end from client to cloud, pinpointing exactly where and why performance may be suffering.
Supporting this intelligence is our Marvis Large Experience Model (LEM), which provides predictive insights into network performance. Trained on vast sets of video collaboration data, the LEM estimates the contribution of critical network features to poor video collaboration user experience in order to predict drops in service quality and identify root cause before user experiences are interrupted. With Marvis LEM and Marvis Minis, observability becomes proactive. Instead of waiting for users to report issues, the network self-tests and predicts failures, keeping experiences smooth and support tickets down.
Marvis AI Assistant conversation interface and self-driving actions speed up troubleshooting
The Marvis conversational interface is another leap forward in empowering the Self-Driving Network. This conversational AI agent redefines how IT interacts with the network, eliminating the need to manually sift through dashboards or use complex CLI. Instead, leveraging agentic AI, large language models (LLMs) and generative AI (GenAI), teams can simply ask questions in natural language and receive clear, actionable answers in response.
Marvis Actions with self-driving actions takes automation to the next level, providing key insights into critical issues impacting the network, providing clear actionable guidance on remediation steps, and, when given permission by the IT team, automatically resolving issues.
In a Self-Driving Network, AI assistants become copilots—providing insights, predicting incidents, and performing tasks to resolve issues and optimize performance proactively. Marvis Actions, Marvis Minis, and the Marvis conversational interface represent our shift toward a Self-Driving Network—one that perceives, reasons, acts, and learns—while keeping humans in the loop and in control.
Leading the way to the Self-Driving Network
A Self-Driving Network is no longer a distant goal. They’re becoming reality, and we’re leading the industry forward. For over a decade, we’ve been relentlessly innovating to reshape networking operations and deliver exceptional, reliable connectivity to users—wherever they are. We purpose built a cloud-native infrastructure to process massive volumes of telemetry, identified the most meaningful data for diagnosing network issues, and trained our Marvis® AI engine and Marvis AI Assistant to act on it. Today, we continue to evolve and deliver new capabilities that push the boundaries of what AI for networking can do.
Our customers are already seeing the impact:
- 90%+ reduction in network-related trouble tickets
- 80% decrease in time spent managing the network
- Lower operational costs, improved network reliability, and faster problem resolution
We’ve led the journey through every stage—from data to insights and recommendations and now to autonomous operations—delivering smarter, more efficient networks at every step.
So, where’s your network on this journey? Let’s take the next step together.
