The importance of network observability in an Agentic AI era

Autonomous networks that can self-heal and self-optimize have long been a tantalizing vision for telecommunications providers, data center operators, and enterprise networking teams. The promise is compelling: AI-native operations where systems diagnose faults, respond to threats, and optimize performance with minimal human intervention. With the rapid advancements across multiple types of AI, the timelines for achieving that vision have compressed. Yet before we can achieve networks that can think for themselves, they need to see clearly. 

Persistent challenges in modern networking 

Despite decades of advancement, network operators continue to grapple with fundamental challenges that resist elegant solutions. 

Operational complexity remains stubbornly high. Modern networks span multi-vendor, multi-generational equipment across multiple domains. Each domain typically has its own architecture and optimization objectives. Enterprise environments add their own layers: hybrid clouds, software-defined WANs, and an ever-expanding constellation of IoT and IIoT endpoints. The “single pane of management glass” remains mythical.

Network security posture grows more precarious as attack surfaces expand. Sophisticated threats disguise malicious traffic within legitimate flows. AI-powered phishing and ransomware campaigns now operate at cloud scale, and encrypted traffic — while essential for privacy — creates blind spots that legacy security tools cannot penetrate. Network teams need defenses that can analyze data in real time and respond before damage occurs, not after. 

Compliance and controls have become increasingly demanding. Regulatory requirements around data sovereignty, privacy, and sector-specific mandates continue to proliferate. Whether it’s HIPAA in healthcare, PCI-DSS in retail, or telecommunications-specific regulations, organizations must demonstrate compliance while maintaining efficiency. This demands fine-grained visibility into traffic, consistent policy enforcement, and comprehensive audit trails. These requirements become even more acute when AI systems start to make autonomous decisions.

To combat these challenges, network operators hope to leverage the latest advancements in AI. Before we discuss the potential of AI to overcome these challenges, let’s take a quick look at the current state of AI in networking. 

The three waves of AI in networking 

AI’s evolution in networking has progressed through three distinct waves, each building on the previous one. Understanding these waves matters because organizations will deploy all three simultaneously, each addressing different operational needs. 

Predictive AI has been with us for years, handling anomaly detection, capacity forecasting, and traffic prediction. Its outputs are analytical: probabilities, classifications, trend projections. Think of it as the analyst telling you: “Based on historical patterns and the current context, network traffic will spike by 20 percent at this node tomorrow."

Generative AI revolutionized how engineers interact with documentation, configurations, and code. It synthesizes new content, including configuration scripts, remediation plans, and policy recommendations. It serves as the assistant that drafts a config to handle that predicted traffic spike. And it can also suggest root causes when examining multiple log files from network systems.

Agentic AI in networking represents the shift from watching and commenting to thinking and doing. These systems operate through continuous observe-orient-decide-act loops, executing plans across infrastructure with minimal human involvement. The agent doesn’t just recommend rerouting traffic; if allowed, it can execute the change and apply QoS policies autonomously in response to a network event.

The value from AI in networking comes from orchestrating these capabilities intelligently. A modern network solution might use predictive AI for capacity forecasting, generative AI for creating remediation plans, and agentic AI for executing those plans across multi-vendor infrastructure. But this orchestration demands something that many AI implementations overlook: comprehensive observability. 

Why observability and content-awareness are critical 

If agentic AI is the brain capable of making decisions, observability is the eyes and ears. Content-awareness —understanding not just that packets are flowing, but what applications and behaviors those packets represent — becomes an important pillar of successful AI adoption.

Accurate decision-making depends on contextual data. An AI agent observing a throughput spike needs to know what constitutes that spike. Is it a legitimate Microsoft Teams update rolling across the enterprise, a data exfiltration attempt, or a DDoS attack in progress? Without content awareness, the agent is guessing based solely on volume. High-fidelity observability transforms raw packet counts into actionable intelligence.

Security visibility requires seeing beyond traditional five-tuple monitoring. In an era where over 90 percent of web traffic is encrypted, agents will have to rely on techniques like behavioral analysis and encrypted traffic intelligence to identify sophisticated threats without decryption. This precision enables security automation that minimizes false positives while catching genuine attacks.

Compliance controls require understanding the nature of application data. An AI agent tasked with optimizing costs must know it cannot route sensitive healthcare data over a cheaper or faster, non-compliant link. Content awareness enforces these guardrails automatically.

Model training and fine-tuning require high-quality ground truth. Networking and security vendors want to advance from generic foundation models (open weights or otherwise) to domain-specific, intelligent, specialized models that can power smarter, agentic workflows. To achieve this, their engineers need accurately labeled data to train AI systems to distinguish between normal network traffic patterns and DDoS attack signatures. 

The Role of Deep Packet Inspection 

Deep packet inspection has emerged as a key technology for delivering the comprehensive, accurate, and timely data that AI-powered networking requires. Unlike conventional filtering that examines only headers, DPI can analyze packet content at the application layer.

Modern DPI solutions combine protocol analysis with machine learning to classify traffic with high accuracy, even if the flow is encrypted, obfuscated, or anonymized. For AI agents, DPI provides the contextual data needed to understand not just what is happening, but why it matters. DPI metadata for traffic flows can be combined with other network data to create data corpora for fine-tuning or pre-training domain-specific network foundation models. These models can be used in generative and agentic flows with better outcomes.

Furthermore, DPI can serve as a real-time data source for agentic AI networks to support accurate decision-making in network optimization, network resiliency, and security tasks.

Organizations evaluating DPI for AI-powered networking should prioritize several capabilities. The solution must handle network scale without introducing latency that impairs agentic workflows. Effective encrypted traffic intelligence is essential. Multi-vendor and multi-domain support ensures visibility across heterogeneous environments. And seamless integration with AI platforms ensures DPI-generated insights flow directly into systems that need them. These capabilities translate directly into practical advantages for different stakeholders. For example, solution vendors developing SASE products can leverage AI-driven features based on real-time, accurately labeled traffic data, eliminating the need to build and maintain proprietary classification engines. Similarly, telecom vendors building 5G network functions or analytics platforms can use DPI-derived metadata to train AI models tailored to their specific network environments.

Conclusion

The journey toward autonomous network operations has been accelerated by advances in AI, but beneath the hype, a fundamental truth persists: the winning strategy isn’t about choosing the flashiest AI platform. It’s about building the data foundations that enable any AI system to succeed. Observability and content awareness, powered by technologies such as deep packet inspection, are critical to transforming AI potential into operational reality.