AI Infrastructure Is Creating a New Wave of Incidents, And Why Enterprises Need a Modern On-Call Strategy
Over the last few years, AI has quietly shifted from a fascinating experiment to a core operational system. Enterprises aren’t just building prototypes anymore — they’re deploying LLMs into production environments where uptime directly affects customer interactions, revenue flows, and business continuity. AI has essentially become a new layer of critical infrastructure.
Because of that shift, the definition of “reliability” is changing. A slow-responding chatbot, an unexplainable spike in inference latency, or a sudden drop in retrieval quality doesn’t feel like a small internal glitch. It feels like an outage. And for many businesses, it is one.
AI infrastructure fails differently — issues in pipelines, GPUs, embeddings, or retrieval can cascade quickly and impact customer-facing systems in minutes.
Monitoring tools detect anomalies, but they don’t guarantee acknowledgment, escalation, or a coordinated human response — the critical gap in AI operations.
Even with agentic AI and auto-remediation, complex or cross-layer failures still require human accountability, auditability, and real-time escalation.
OnPage fills this reliability gap by ensuring high-priority alerts reach the right engineer immediately, persist until acknowledged, and follow structured escalation paths.
With full audit trails, post-incident reporting, and schedule-driven routing, OnPage helps AI teams respond faster, stay accountable, and keep LLM-powered systems running reliably.
A Stack With More Moving Parts, and More Ways to Falter
AI infrastructure looks deceptively clean from the outside: a model that takes an input and returns an answer. But under the hood, the stack has grown into a dense network of components that must all stay aligned for the system to function.
GPU clusters, vector databases, orchestration layers, data pipelines, feature stores, and model gateways are all working simultaneously — each one relying on the others to deliver the right output at the right time. When one piece slows down or drifts out of sync, the entire chain starts to wobble.
A delayed data pipeline might seem harmless on its own, but that delay can produce stale embeddings, which then degrade retrieval accuracy. A GPU pool hitting a memory threshold can cause inference delays that ripple up to customer-facing apps. A schema change in one upstream service can break a RAG application hours later, without any obvious cause.
AI systems don’t just fail — they compound. A minor issue upstream becomes a user-facing incident downstream faster than anyone expects.
Why AI Failures Are Harder to Catch (and Harder to Respond To)
Traditional software outages usually announce themselves: a service crashes, a CPU spikes, a dependency stops responding. AI failures are more subtle. They degrade quality before they degrade functionality, and they rarely map cleanly to a single domain or owner.
Most enterprises now rely on robust monitoring stacks, everything from GPU telemetry to drift detection to data quality checks. These systems do a great job telling teams what is going wrong.
But detection alone doesn’t keep systems running.
Monitoring Detects. OnPage Mobilizes.
Observability platforms surface GPU hot spots, drift signals, pipeline delays, and anomalies in retrieval. They show what’s breaking, but they don’t ensure anyone responds. That last step, coordinating the human response with clear acknowledgment, escalation, routing, and accountability, is where AI reliability often breaks down.
In many AI incidents, the difference between a “weird blip” and a full system outage isn’t the monitoring tool. It’s whether the right engineer sees the alert in time, acknowledges it, and takes action.
This is where OnPage fits: bridging the gap between detection and response.
AI Helps, But It Can’t Replace Humans in On-Call
So even as AI becomes more capable, from routing alerts to predicting failures to triggering auto-remediation workflows, there’s still a limit to how far automation can go. Agentic systems can restart services, rebalance GPU loads, refresh stale indexes, or temporarily bypass a failing retrieval endpoint. But they can’t fully replace the human responsibility layer.
AI can help decide which alert matters most. AI can help predict which component is drifting. AI can even try to self-heal the first few symptoms.
But AI can’t be accountable. It can’t sign off on a regulatory action. It can’t take responsibility for a multi-layer AI outage. And it definitely can’t own an incident end-to-end the moment things get messy.
As AI infrastructure grows more interconnected, the need for a human in the loop becomes even more essential, not less. And that means organizations still need a reliable, traceable way to notify actual people when AI systems cross a threshold that automation can’t safely handle. On-call isn’t going away; it’s evolving.
AI Outages Aren’t Quiet. They’re Immediately Visible.
Another major shift is the visibility of AI problems. Unlike backend services that fail silently, AI failures show up directly in front of users. A chatbot starts producing irrelevant answers. An agent-assist tool becomes slow or unresponsive. A recommendation engine surfaces off-target results. A healthcare triage tool misinterprets symptoms.
When AI stumbles, users notice immediately. And because businesses increasingly rely on these systems for customer service, triage, automation, decision-making, and internal workflows, the blast radius of an incident is bigger than ever.
This is why enterprises are moving from “monitor AI” to “reliably operate AI.” The difference is night and day.
Reliability Now Belongs to Multiple Teams — Not One
One of the most challenging realities of AI operations is that responsibility is distributed. Keeping an AI system reliable involves Cloud Infrastructure, Data Engineering, MLOps, AI Platform teams, SREs, and governance groups — all owning different slices of the stack.
An indexing issue might begin in a data pipeline, surface in a RAG application, and end up in a customer support workflow. A GPU bottleneck might start in a compute cluster but only become visible when model latency spikes.
With so many touchpoints, the biggest operational problem isn’t lack of monitoring — it’s lack of clear ownership at the moment something goes wrong.
Where Traditional Alerting Breaks Down
Despite how advanced AI infrastructure has become, many teams still rely on basic notification channels: Slack pings, emails, dashboard alerts, or homegrown scripts. These channels were never designed for cross-functional incidents, rapid escalation, or structured response — and they certainly weren’t built with AI’s compounding failure patterns in mind.
What’s often missing is:
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Clear routing to the right engineer
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Accountability for who acknowledged what
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Escalation paths that activate when someone doesn’t respond
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After-hours reliability
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Traceability for audits and post-incident analysis
When AI systems are involved, missing an alert by even a few minutes can turn a subtle quality issue into a full outage.
This Is Where OnPage Fits: Mobilizing Response When AI Falters
AI infrastructure now demands the same level of rigor traditionally reserved for financial, healthcare, or hyperscaler cloud systems. OnPage adds that reliability layer, not by replacing monitoring tools, but by connecting them to human action.
When a drift signal triggers, a pipeline slows, a GPU pool hits a threshold, or a vector database starts returning inconsistent results, OnPage ensures that alert reaches the right person immediately, and continues until it is acknowledged. Escalation paths ensure that if the first on-call engineer doesn’t respond, the next one will.
Because every acknowledgment, handoff, and timeline is captured automatically, AI teams gain the auditability and accountability they need, especially in environments under regulatory or governance pressure.
Post-incident reports help teams understand patterns across data, compute, models, and infrastructure so they can strengthen reliability long-term.
In a world where AI systems can degrade quietly and break quickly, simply detecting issues isn’t enough. Responding to them, consistently, clearly, and with full traceability, is now a core competency.
And Behind It All: The Physical Infrastructure Matters Too
The software stack is only half the story. AI also depends on massive physical infrastructure — high-density GPU clusters, data warehouses, cooling systems, and electrical redundancy. Outages at this layer can be just as disruptive as failures in data pipelines or inference gateways.
This physical side of AI reliability deserves its own deeper look. We’ll unpack that in the next blog, where we’ll explore how facility-level incidents impact AI workloads and what organizations can do to prepare.
The Shift From Observing AI to Operating AI
AI has introduced a new type of reliability challenge: cross-functional, fast-moving, and compounding incidents that require rapid and accountable response. Monitoring tools detect these issues. OnPage mobilizes the right teams to fix them.
As enterprises scale LLM-powered systems, this operational maturity becomes essential. Keeping AI systems healthy isn’t just about detection — it’s about ensuring the right person responds, every time.
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