Operations | Monitoring | ITSM | DevOps | Cloud

Large language models (LLMs) have transformed the way we interact with technology, impacting how we generate reports and documents, understand complex topics, and even how we search the internet. But in network operations, where every minute of downtime can mean lost revenue and productivity, a generic LLM isn’t enough.

The observability market is changing rapidly. The days of simply collecting logs, metrics, and traces are giving way to something bigger: delivering actionable intelligence that actually connects IT operations with business goals. Organizations don’t just want to know what’s happening anymore; they need to understand why it’s happening, what actions to take, and whether their systems can respond independently.

In the early days of any IT operation, pragmatism rules. Most network teams start with what’s readily available—custom scripts, Excel spreadsheets, shared network drives, and tribal knowledge. It’s cost-effective and familiar. But as your organization grows, so does the complexity of your network. Devices multiply, configurations diversify, and the operational risk of keeping everything “stitched together” with manual methods increases exponentially.

Network visualization tools are software applications that allow users to represent, explore, and analyze network structures graphically. These networks can include computer and telecommunication infrastructure, as well as social, biological, and organizational networks. Visualization is achieved by displaying nodes (entities) and edges (relationships), making complex datasets easier to interpret and manage.

As government agencies modernize IT infrastructure, many are shifting to hybrid and multicloud environments. But this evolution brings heightened exposure to cyber threats. For the public sector, where data protection is tied to national security and public trust, compliance is more than a box to check—it’s the front line of defense. FedRAMP (Federal Risk and Authorization Management Program) provides a standardized framework for securing cloud services used by U.S. agencies.

Imagine if degrading network performance—not just bad code—disrupted your live stream during a high-profile event. Customers start flooding support lines. Social media lights up. Your NOC teams scramble to identify the root cause amid fragmented systems. The outage impacts not only your broadcast, but also subscriber logins, ad delivery, and mobile apps. Advertisers want refunds. Executives ask, “Why didn’t we see this coming?”

In the first two parts of this series, we explored why modern network operations demand intelligent automation and how AI agents can reason, act, and collaborate to solve complex problems. We examined the frameworks – such as ReACT, LangGraph, and Pydantic – that power these agents, and how the Model Context Protocol (MCP) facilitates seamless integration with tools and services. But theory alone doesn’t improve network uptime or reduce manual toil.

Organizations migrating from Opsgenie and other legacy incident management platforms are discovering that basic connectivity monitoring isn’t enough for modern operations. While Opsgenie Heartbeats and similar traditional heartbeat features offer simple binary status checks of system availability, PagerDuty’s AIOps-powered approach transforms system health monitoring from reactive alerting into intelligent, automated operational intelligence.

There’s no shortage of hype around AI in operations, but recognition from a trusted source like GigaOm cuts through the noise. We are excited to share that PagerDuty earned a top spot as a Leader and Outperformer in the 2025 report. It’s recognition that reflects the progress we’ve made in delivering an AI-powered platform that actually helps teams move faster, reduce costs, and operate with confidence in complex environments.