Partner POV | Building AI Observability from the Packet Up

This article was written and contributed by, NETSCOUT.

Cut through AI complexity with real-time, ground-truth observability data.

Imagine trying to navigate a dense fog without a compass. That's what managing artificial intelligence (AI) systems and modern applications can feel like without consistent, reliable insights. Observability can help technology teams and data engineers verify that these systems and the networks they rely on are performing as expected. Without this deeper context, traditional monitoring tools struggle to keep up with dynamic, adaptive AI models.

Because their accuracy and reliability depend heavily on the quality of the data they are trained and run on, AI models continuously adjust based on real-time inputs. Packets provide the ground truth teams need to monitor, validate, and optimize AI outcomes, while also enabling faster troubleshooting.

Inside-Out and Outside-In Observability

Observability commonly covers three complementary domains: infrastructure monitoring to verify network and platform health, application performance monitoring to track transaction processing and service interactions, and user experience monitoring to validate what customers actually see and feel. Aligning these dimensions unifies silos and helps information technology (IT) and AI teams trace issues from their visible impact back to root causes. AI workloads introduce unique observability challenges including model drift, inference latency, and real-time data quality—all of which require comprehensive monitoring, including infrastructure, applications, and users.

Outside-in observability 

Outside-in observability measures system behavior from the user's perspective. This approach focuses on the direct symptoms of performance degradation as experienced by the end user at the service edge, indicating potential underlying issues within the system architecture. It leverages techniques such as:

• Synthetic testing
• Journey tracking
• Application monitoring

These techniques help pinpoint where delays or failures occur during typical usage. They surface symptoms that may impact customers or disrupt business operations, while also providing clear benchmarks for response times and availability aligned with business priorities. While outside-in observability captures what the user experiences, inside-out observability reveals the underlying system activity.

Inside-out observability

Inside-out observability focuses on the infrastructure, applications, and network paths that support the users. It draws on telemetry, flow data, and granular, packet-level inspection to uncover how systems communicate, how protocols behave, and where dependencies might break down under stress and degrading performance.

Packet-derived data strengthens this inside-out monitoring by showing:

• Who is communicating
• Which protocols are used
• When exchanges happen
• How long interactions take
• Where errors, retries, or anomalies occur

By combining these details with outside-in measurements, teams gain a complete, end-to-end understanding of service-layer and network-level performance. This reduces time spent on finger-pointing and guesswork in complex, real-time environments, which are often the proving grounds for emerging AI technologies.

Enriching Observability from the Packet Up

Packets are irreplaceable indicators of network and service behavior and AI workloads, especially in emerging areas such as agentic AI and multimodal models. They capture full transaction details, including timing, retransmissions, and protocol errors. Unlike metrics or logs that only summarize activity, packets show exactly what was sent, when it happened, and how systems respond across the entire IT stack.

This critical level of visibility supports full session reconstruction, giving IT teams the ability to replay entire conversations between services. It is especially valuable for maintaining service levels across hybrid architectures, where traffic may cross both on-premises and cloud environments with different dependencies.

When packet data is enriched as metadata, teams create a data backbone that supports consistent, high-quality observability across the environment. This provides actionable evidence for identifying root causes, proactively detecting performance regressions, and validating service behavior under load. Teams gain a defensible source of truth, reduce mean time to resolution, and improve consistency for AI-driven workloads and other critical business applications.

Unlocking AI Insights with NETSCOUT

AI is evolving fast. The AI observability market is projected to grow at a compound annual growth rate (CAGR) of 14.59 percent from 2025 to 2030. To keep pace, observability must deliver accurate, immediately actionable insights. NETSCOUT's deep packet inspection (DPI) technology helps customers harness the massive volume of their network data and make informed decisions by converting it into protocol-aware metadata in real time. NETSCOUT Omnis AI Insights builds on this by generating and streaming a curated, high-fidelity dataset to platforms such as Splunk and ServiceNow, helping organizations adapt more effectively for AI.