How to Use AI Monitoring: A Practical Walkthrough

Why AI Monitoring Is Now Non-Negotiable

Your AI infrastructure is probably already under strain — you just can't see it yet. Ninety percent of surveyed IT leaders are now deploying generative AI, and the systems supporting those workloads are buckling faster than traditional monitoring can catch.

The numbers tell a stark story. In a survey of 350+ IT leaders, Flexential's 2025 State of AI Infrastructure report found that 44% cite IT infrastructure constraints as the top barrier to expanding AI initiatives. Meanwhile, network problems that were once secondary concerns have surged: bandwidth issues now affect 59% of respondents (up from 43%), and latency challenges jumped from 32% to 53% in a single year.

Why does manual monitoring fail here? Three reasons:

• The pace of change outstrips human baselines. AI workloads create dynamic, spiky demand patterns that static thresholds and dashboard-watching simply can't keep up with.
• The failure modes are novel. GPU saturation, bandwidth bottlenecks, and latency spikes behave differently from classic web traffic and need anomaly detection, not fixed alerts.
• Security exposure is rising alongside scale. 55% of IT leaders say AI has increased their exposure to cyber threats (up from 39%), and 46% admit their cybersecurity teams lack full AI security expertise.

That last point deserves emphasis. AI hasn't just strained infrastructure — it has expanded attack surfaces, with 51% of leaders saying exactly that. When your own security team lacks the expertise to spot AI-specific threats, automated anomaly detection stops being a nice-to-have and becomes your first line of defense. As Flexential's guidance on AI infrastructure puts it, traditional infrastructure "lacks the native tooling required to fully support AI's complex demands."

There's also a talent problem compounding everything. Only 14% of respondents say they have the right talent, and 61% report shortages in managing specialized computing infrastructure. When you can't hire enough people to watch the systems, you need systems that watch themselves.

This is why AIOps — AI-driven monitoring that combines anomaly detection, intelligent alerting, root-cause analysis, and increasingly autonomous remediation — has moved from experimental to essential. DevOps is, as one industry analysis noted, "a data-rich, back-office practice that presents a perfect sandbox" for AI, because computer systems produce the precise, measurable values that machine learning handles well.

The good news: the pattern is well established. Modern AI monitoring follows a recognizable pipeline — collect telemetry, baseline normal behavior, detect anomalies, route alerts, diagnose root causes, and optionally auto-remediate. The rest of this walkthrough shows how to build exactly that, step by step.

The Six-Stage AIOps Pattern Every Platform Follows

Whether you buy Datadog, Dynatrace, New Relic, or assemble an open-source stack, every serious AI monitoring platform follows the same underlying shape. Understanding that shape is what turns "we should monitor with AI" from a buzzword into a buildable plan.

Think of it as a six-stage pipeline, described across the leading AIOps platforms and echoed in Mirantis' framing of monitoring as the final stage of the AI data journey:

• Collect telemetry via connectors. Agents and collectors pull metrics from databases, VMs, networks, cloud services, and on-prem hardware. CIO.com's core evaluation criterion is exactly this: look at the range of connectors and how well the platform integrates with your particular services.
• Baseline normal loads. Machine learning builds a picture of what "normal" looks like — Datadog, for example, forecasts from historical records adjusted for season and time of day.
• Detect anomalies. The platform flags deviations in latency, RAM, bandwidth, or error rates against that baseline, rather than static thresholds alone.
• Route alerts. Notifications flow to email, Slack, or a pager — with configurable sensitivity. New Relic's pattern is instructive: low-priority errors only alarm after several occurrences in 15 minutes, while a crashed server pages immediately.
• Correlate for root cause. Instead of drowning in 50 alerts from one failure, the platform deduplicates and maps events across services — Moogsoft, for instance, correlates events from New Relic, Datadog, AWS CloudWatch, and AppDynamics before raising an alarm.
• Optionally auto-remediate. Dynatrace's AutomationEngine can trigger autonomous actions like rebooting an instance — though CIO.com reports practitioners are split, with some managers finding it "unnerving" to give systems that much leeway.

This pipeline matters now more than ever because the infrastructure it watches is under real strain. In a survey of 350+ IT leaders, 44% cited IT infrastructure constraints as the top barrier to AI initiatives, bandwidth issues hit 59%, and latency challenges jumped from 32% to 53% year over year. Anomaly detection and alerting are designed precisely to catch these failure modes before they cascade.

Two caveats keep expectations honest. First, vendor performance claims — such as Digitate ignio's estimate that 40% of issues are handled proactively with 60% less manual effort — are marketing figures, not independently benchmarked results. Second, the detailed setup steps (config syntax, dashboard provisioning) live in each tool's own documentation; the pipeline above is the durable part.

The good news: once you know what to monitor, much of the configuration work — generating alert rules, drafting runbooks, summarizing incidents — is automatable with agent skills. SkillzBench is building a free, security-scanned directory of verified agent skills plus a free MCP connector to wire them into your agent; join the waitlist to be first in line.

Getting Started: Prometheus + Grafana, Then the AIOps Upgrade Path

The open-source foundation for AI monitoring starts with a simple, robust stack. Prometheus and Grafana remain the standard choice for real-time visibility and SLA enforcement in dynamic workloads. Mirantis explicitly recommends integrating these tools to ensure model performance meets strict service level agreements. This setup provides the critical baseline data needed to trigger automatic resource scaling when demands spike.

Once you establish this baseline, you can evaluate commercial AIOps platforms for advanced anomaly detection. The primary evaluation criterion is the range of available connectors. You must verify how well each tool integrates with your specific databases, cloud services, and network infrastructure. For example, LogicMonitor offers over 3,000 integrated collectors spanning VMs, data lakes, and on-premise hardware. This breadth ensures no blind spots in your telemetry collection.

Alert configuration is where most teams struggle with noise. A practical starting rule involves configurable sensitivity thresholds to balance urgency with relevance. New Relic illustrates this pattern effectively: low-priority errors only trigger an alarm after several occurrences within 15 minutes, while a crashed server pages an engineer immediately. This tiered approach prevents alert fatigue while ensuring critical failures never go unnoticed.

Consider these integration checks before committing to a commercial platform:

• Verify the platform supports your specific database versions and cloud providers.
• Confirm it can ingest telemetry from both on-premise and hybrid cloud environments.
• Test the alert routing capabilities to ensure correct escalation paths for different severity levels.
• Assess the root-cause analysis features to see if they correlate events across services.

As AI infrastructure expands, the need for precise monitoring grows. Industry reports indicate that 59% of IT leaders now face bandwidth issues, up from 43% previously. Latency challenges have also jumped to 53%, making real-time visibility essential for maintaining reliability. Without this granular data, teams cannot predict capacity needs or mitigate rising cyber-threat exposure.

The monitoring workflow itself is ripe for automation. Agent skills can handle the repetitive tasks of writing scrape configurations, generating alert rules, and drafting incident runbooks. Recent platform developments show that agentic architectures are increasingly linking with open standards like the Model Control Protocol. This trend aligns with how tools like SkillzBench are building free, verified directories for agent skills. By connecting agents to these skills, you can automate the complex setup processes that traditionally consume valuable DevOps time.

Tuning Alerts Without Fatigue — and Deciding How Much to Automate

Alerts are where most AI monitoring projects quietly succeed or fail, because a good part of the AIOps stack exists purely to suppress noise and ensure only significant problems interrupt a meeting or a good night's sleep, as CIO.com's AIOps platform roundup puts it. Get thresholds wrong and you train your team to ignore the pager; get them right and monitoring earns trust.

Start with sensitivity tiers rather than one global threshold. New Relic's configurable approach is a copyable pattern: low-priority errors alarm only after several occurrences within a fifteen-minute window, while a crashed server pages immediately. Datadog applies machine-learning forecasts based on historical records adjusted for season and time of day, alerting on latency, RAM, or bandwidth deviations rather than fixed numbers.

Deduplication and correlation come next. Moogsoft integrates with New Relic, Datadog, AWS CloudWatch, and AppDynamics, deduplicating and correlating events before raising alarms. That single step removes the duplicate storms that make on-call rotations miserable.

Then stage autonomy at your own comfort level, because practitioners genuinely disagree here:

• Human-in-the-loop first: alerts route to a person, with recommended runbook steps attached.
• Assisted remediation: the system proposes an action and waits for one-click approval.
• Bounded autonomy: pre-approved actions like restarting a service or rebooting an instance execute automatically.
• Full self-healing: the platform clears tickets on its own.

That last stage divides teams. CIO.com reports some managers find it unnerving to give AIOps systems too much leeway, while others are captivated that the machines can clear more IT tickets by themselves. Both reactions are reasonable, and your risk tolerance should set the ceiling, not vendor enthusiasm.

Once you know what to monitor and how far to automate, agent skills can handle the rest of the workflow — generating alert rules, drafting runbooks, summarizing incidents. SkillzBench is building a free, continuously verified directory of agent skills plus a free MCP connector, with a waitlist open now.

Automate the Monitoring Workflow Itself with Agent Skills

You have the monitoring pattern nailed down — telemetry in, baselines built, anomalies detected, alerts routed, root causes traced. The remaining problem is that every new service, dashboard, and alert rule still needs a human to write it by hand.

That configuration work is exactly where AI agents are starting to earn their keep. The same agentic architectures that platforms like Splunk AppDynamics link through open standards such as the Model Context Protocol, or MCP, let an agent read your environment and draft the artifacts around it. Instead of hand-authoring scrape targets, alert thresholds, and runbooks for each service, you describe intent and let the agent produce a reviewable first pass.

In practice, that means agents can take on a surprising share of the monitoring workflow:

• Drafting scrape and collector configurations for new services as they ship
• Generating alert rules with sensitivity tuned to each signal's blast radius
• Writing runbooks that pair each alert with a concrete first response
• Summarizing incidents after the fact, so postmortems start from a draft instead of a blank page

The stakes justify the automation. Flexential's survey of 350+ IT leaders found 44% cite infrastructure constraints as the top barrier to scaling AI, while 59% report bandwidth issues and 53% report latency challenges. Those are precisely the failure modes monitoring exists to catch — and the configuration backlog is what stops teams from catching them fast enough.

This is also where verification matters as much as generation. Agent skills are proliferating faster than anyone can vet them, and published research has found roughly 26% carry at least one vulnerability. SkillzBench is building a free, independent directory that pins every skill to a commit SHA, security-scans and quality-judges it, and benchmarks each one in a sandbox to publish its Lift in percentage points — with a free MCP connector that lets any agent see only the skills you choose. Everything is free; revenue comes from advertising around the blog, never from scores or listings.

SkillzBench is pre-launch, so treat this as a preview rather than a live product. The waitlist is open, and joining it is the way to be first in when the directory and connector go public.