What is the field of AI Ops / Network Observability?
Historical Definition
The historical definition of AIOps describes the application of artificial intelligence, machine learning, and big data analytics to enhance and automate IT operations processes.
Origin and Evolution: The term has undergone a specific evolution since its inception by the research firm Gartner:
- 2016 (Initial Coining): Gartner first introduced AIOps as shorthand for "Algorithmic IT Operations". At this stage, it was defined as the application of algorithmic processes and big data to address the growing complexity of modern IT infrastructure that traditional monitoring tools could no longer handle. This was an evolution from the more traditional network monitoring and observability definitions that the field was encapsulated by in the earlier parts of the 2000s.
- 2017 (Rebranding): Within approximately one year, Gartner modified the acronym to stand for "Artificial Intelligence for IT Operations". This shift reflected a move toward more advanced AI capabilities beyond simple mathematical algorithms, emphasizing self-learning and predictive modeling.
Historically, AIOps is defined as a multi-layered technology platform that automates three primary IT disciplines:
- Big Data Ingestion: Scalable collection of a wide variety of data (logs, metrics, events, and tickets) from disparate IT components.
- Machine Learning/Analytics: The use of algorithms to perform automated event correlation, anomaly detection, and causality (root cause) determination.
- Automated Remediation: Enhancing IT service management (ITSM) and performance management by triggering responses to issues in real-time with minimal human intervention.
For physical layer networks - this has often been conflated with network observability and telemetry as a catch all field for utilizing the three IT disciplines above to create reliability and operational oversight into complex internet and cloud infrastructure.
Use Cases for Network-centric AI Ops or NetOps
In 2025, Network-Centric AIOps (often called AI-driven Networking or NetOps AI) focuses on the physical and logical transport layers of the stack. While application-centric AIOps cares about "Is the website slow?", network-centric AIOps asks "Is there a routing loop, packet loss on a specific trunk, or a misconfigured VLAN?"
- Intelligent Fault & Anomaly Detection
- Dynamic Thresholding: Traditional monitoring uses static alerts (e.g., alert if CPU > 80%). Network AIOps learns "normal" patterns for specific times of day and only alerts on true anomalies, such as a sudden burst of micro-burst traffic that would normally be missed.
- Blast Radius Analysis: When a core switch fails, AIOps uses real-time topology mapping to immediately identify exactly which subnets, VLANs, and downstream devices are affected, preventing engineers from having to manually trace cable runs or logic maps.
- Automated Network Remediation (Closed-Loop)
- Self-Healing Routes: If the AI detects packet loss or high jitter on a primary ISP link, it can automatically trigger a reroute to a secondary path via SD-WAN or BGP policy shifts before users report a "slow connection".
- Automated Configuration Rollbacks: Many network outages are caused by manual config errors. AIOps can detect a failure immediately after a change and autonomously roll back to the last known good configuration state.
- Performance & Capacity Optimization
- Predictive Traffic Engineering: In 2025, AIOps uses AI inference models to forecast bandwidth spikes. It can pre-emptively adjust Quality of Service (QoS) rules or provision additional virtual bandwidth to ensure critical traffic (like VoIP or medical imaging) is prioritized during peak loads.
- Predictive Maintenance: By analyzing environmental data (fan speeds, optical light levels, power draw), AIOps identifies hardware that is likely to fail in the next 48 hours, allowing for proactive replacement.
- Security-Network Convergence (SecOps)
- Traffic Pattern Profiling: AI identifies "east-west" lateral movement indicative of a breach. For example, if a printer suddenly starts communicating with a database server over an unusual port, the network can automatically micro-segment and quarantine that device.
- DDoS Mitigation: Network AIOps can distinguish between a legitimate "flash crowd" and a distributed denial-of-service attack, automatically applying rate-limiting at the network edge.
- Modern Telemetry & Interaction
- Streaming Telemetry (MDT): Moving away from slow SNMP polling, AIOps in 2025 leverages gRPC/gNMI push-based telemetry for sub-second visibility into network states.
- Natural Language Troubleshooting: Network engineers can use "Generative AI Copilots" to ask: "Show me all interfaces with CRC errors in the New York data center" or "Why did the BGP session drop on Router A?" and receive a technical summary plus a fix recommendation.
More to come on this last point as we get into why LLMs are creating a new class of observability and monitoring tooling at all layers of the IT stack.
Incumbent Industry SaaS Providers
The latest Magic Quadrant for network observability / AIOps highlights something that’s easy to miss if you focus only on “leaders” versus “laggards”: observability has matured into a horizontal infrastructure layer, not a problem-specific solution category.
On the same chart, you see:
- Vendors that originated 15-20 years ago (Splunk, IBM, SolarWinds, Oracle)
- Cloud-era incumbents from the 2010-2015 wave (New Relic, Datadog, Elastic)
- And a newer generation of opinionated, developer-first tools (Honeycomb, Chronosphere, Coralogix)
That convergence explains both the category’s success—and its growing limitations.
Observability Platforms Became Catch-All Data Pipes
Most leading observability tools today were not designed around a single, concrete operational outcome. Instead, they evolved as:
- High-throughput data ingestion engines
- Time-series and log storage systems
- Query layers with flexible dashboards and alerts
This makes them incredibly powerful—but also fundamentally generic.
In practice, these platforms function more like: (1) A data lake for telemetry, or a (2) streaming analytics substrate rather than tools that are natively opinionated about what action should happen next.
As a result:
- Customers spend significant time deciding what to monitor
- Teams manually define thresholds, dashboards, and alerts
- Remediation often lives outside the platform—in runbooks, Slack threads, or tribal knowledge
Observability answers “What happened?” very well. It answers “What should we do?” much less reliably.
Why do LLMs and Modern AI Change this ?
LLMs change observability by adding operational context on top of metrics, logs, and traces.
Traditional platforms fire alerts based on thresholds — packet loss, latency spikes, error rates—then rely on engineers to correlate those signals with tickets, maintenance windows, and prior incidents. AI systems can do that correlation automatically.
Pushing from Dashboards to Root Causes Instead of treating a latency spike as an isolated event, AI can link it to a known carrier maintenance, a recent configuration change, or a recurring failure pattern seen in past incidents. Alerts move closer to root cause, not just symptom detection.
Tickets Become Operational Signal Incident tickets, NOC notes, and postmortems become structured input. When connected to telemetry, they help predict repeat outages, prioritize alerts by customer impact, and distinguish between chronic issues and one-off events.
Reducing Alert Fatigue at Scale Large enterprises and service providers ingest thousands of carrier maintenance notices and service advisories every month. Most are ignored today. LLMs can continuously parse this data to suppress “expected” alerts and surface anomalies that fall outside planned work.
Replacing Static Runbooks for actual orchestration Legacy automation depends on static runbooks, wiki pages, and template-based workflows (Jinja, custom scripts, tools like SquadCast). AI-driven orchestration engines can reason in real time—choosing remediation steps based on topology, past outcomes, and live conditions.
There is a lot to be excited for in space over the coming years, this Supertrace blog series will be dedicated to analyzing both technical and business advancements in the field. Our aim is to catalog best practices so that network engineers and reliability engineers all over the globe can benefit and usher in the age of autonomous networking and observability! So stay tuned.