Let’s face it. The modern network is a beast — a sprawling, complex organism of clouds, data centers, SaaS apps, home offices, and, depending on your industry vertical, factories, offices, retail locations, or branches. Mix in the internet as the backbone to connect them all, as well as an ever-increasing volume and velocity of data, and it becomes clear that traditional monitoring tools are now akin to peering through a keyhole to look at a vast landscape.
They simply can’t see the bigger picture, and a new approach is needed: Enter Artificial Intelligence (AI), the game-changer ushering in a new era of Network Intelligence.
From Reactive to Intelligent: The AI Revolution
Remember the days of watching hundreds of dashboards, sifting through endless logs, and deciphering cryptic alerts? Those days are fading fast. Machine Learning and Generative AI are transforming network observability from a reactive chore to a proactive science.
ML algorithms, trained on vast datasets of enriched, context-savvy network telemetry, can now detect anomalies in real-time, predict potential outages, foresee cost overruns, and even identify subtle performance degradations that would otherwise go unnoticed. Imagine an AI that can predict a spike in malicious traffic based on historical patterns and automatically trigger mitigations to block the attack and prevent disruption. That’s a straightforward example of the power of AI-driven observability, and it’s already possible today.
But AI’s role isn’t limited to number crunching. GenAI is revolutionizing how we interact with network data. Natural language interfaces allow engineers to ask questions like: “What’s causing latency on the East Coast?” and receive concise, insightful answers.
Kentik Journeys takes this further, offering an approachable, AI-augmented user experience with deep network context. By leveraging ML and GenAI and using our vast and uniquely enriched data, we provide insights into network behavior: surfacing anomalies, providing probable causes, and offering actionable recommendations for cost and performance optimizations.
Agentic AI Takes Center Stage
But the real revolution is yet to come. Imagine a network in which AI isn’t just a tool but an active participant, a digital colleague working alongside human engineers. This is the promise of agentic AI.
These aren’t your typical AI algorithms. Agentic AI systems possess a degree of autonomy, allowing them to make decisions and take actions within a defined framework. Think of them as digital network engineers, initially assisting with basic tasks but constantly learning and evolving, making them capable of handling routine assignments, troubleshooting fundamental issues, or optimizing network configurations.
For example, an agentic AI, noticing asymmetric routing in a cloud environment (which can add unnecessary cost), could initiate (or recommend to the human for final approval) a configuration change, creating an appropriate route in the cloud account to leverage existing VPC peering to reduce costs and improve performance.
At Kentik, our robust analytics and visualization capabilities provide the perfect foundation for developing an agentic AI for networks. Our deep understanding of network state and behavior can be used to train these AI agents, enabling them to make increasingly complex decisions and to take appropriate actions, leveraging agentic AI directly and with partners.
Efficiency, Scalability, and Intelligence
The advantages of AI-driven network intelligence include the following:
- Proactive Insights: Detect anomalies before they impact users, preventing costly downtime, recommending fast remediation, and ensuring a seamless user experience.
- Enhanced Efficiency: Reduce manual effort, freeing engineers to focus on strategic initiatives.
- Improved Scalability: You can effortlessly handle the ever-growing volume of network data, simplifying the management of complex hybrid and multi-cloud deployments.
- Operational Intelligence: Gain a holistic view of network health, enabling data-driven decisions for capacity planning and cost and performance optimization.
Use Cases: From Troubleshooting to Autonomous Optimization
The applications of AI in network observability are vast and varied:
- Root Cause Analysis: Pinpoint the source of network problems, correlating events and metrics to identify the root cause.
- Predictive Analytics: Anticipate potential issues from historical trends and proactively take steps to mitigate them.
- Cost and Performance Optimization: Identify bottlenecks and optimize traffic flows to ensure optimal application performance and minimize cost
- Security Enhancement: Detect and respond to security threats in real-time, protecting critical infrastructure from attacks.
With the advent of agentic AI, these use cases will expand even further. Imagine AI agents collaborating with human engineers, eliminating the drudgery and allowing humans to focus their attention and creativity on what matters.
Navigating the AI Landscape
While the potential of AI is immense, there are challenges to address:
- Data Quality: AI agents and algorithms are only as good as the data they are trained on. Ensuring data accuracy and completeness is crucial. Validation is a necessary part of agentic AI systems.
- Explainability: Understanding how AI models arrive at their conclusions is essential for building trust and ensuring responsible use.
- Ethical Considerations: As AI agents become more autonomous over time, it’s critical to establish clear guidelines and ensure they operate within defined boundaries.
Addressing these challenges upfront as part of the design and development phases of AI initiatives is paramount [and that is precisely how we have gone about it at Kentik].
The Future: Network Intelligence
The future is network intelligence underpinned by AI. This will eventually enable networks that can self-heal, self-optimize, and adapt to changing conditions with little human intervention. In the near term, Agentic AI, personified by digital network engineers, will become an integral part of network operations, collaborating with human engineers to create a more efficient, reliable, and secure network infrastructure. This, in turn, will pave the way for a new and exciting era of digital innovation.
This is not science fiction. It’s the future we are building today.
The post AI in Network Observability: The Dawn of Network Intelligence appeared first on The New Stack.