AI Infrastructure Is Creating a New Wave of Incidents, And Why Enterprises Need a Modern On-Call Strategy

Over the past couple of months, my entire world has felt flooded with AI breakthroughs. Everywhere I look — podcasts, Twitter, YouTube — it’s another debate about whose chips are faster, whether Blackwell really leapfrogs H100s, if TPUs are finally catching up, or which AI-first startup is about to upend another industry. My podcast feed alone is a buffet of Suno reshaping music, Synthesia reinventing video creation, and yet another “generation-defining” model dropping every other week. And in my own day-to-day, I’m constantly impressed by how fast tools like ElevenLabs, AirOps, and Synthesia are pushing efficiency to new levels. Even Momentic — which I haven’t personally used but understand better now after talking with my PM — makes it obvious how quickly this space is moving.

But beneath all the excitement, there’s this quieter, slightly less glamorous reality that doesn’t show up in the demo videos. When you’re an AI-first company, your product isn’t “just a model.” It’s this sprawling, interconnected system made of data pipelines, embeddings, vector databases, GPU clusters, inference gateways, agent logic, and all the orchestration glue holding it together. And the moment your product is the model, reliability takes on an entirely new personality.

A spike in inference latency?
A stale embedding index?
A pipeline delay?
A GPU pool hitting a threshold

None of these are theoretical issues anymore. They instantly become user-facing problems, often in real time. AI failures don’t behave like traditional software outages. They degrade quietly, compound quickly, and turn into full-blown incidents before most teams know where to look.

And that’s where this story really begins.

A Stack With More Moving Parts, and More Ways to Falter

From the outside, AI products look magical: you send a prompt, something brilliant comes back. But behind that simplicity is a messy, highly sensitive stack that has to stay perfectly aligned for your product to behave predictably.

You’ve got:

• GPU clusters handling unpredictable inference loads

• data pipelines generating embeddings

• vector databases storing and retrieving them

• orchestration systems chaining everything together

• model gateways juggling concurrency

• agent frameworks running multi-step reasoning

• indexing workflows updating constantly

Every component is talking to another. Every dependency relies on timing, freshness, and throughput. Every small issue creates a ripple.

A slight delay upstream, say, a delayed pipeline run, produces stale embeddings, which hit your vector DB, which returns less relevant results, which causes your RAG system to degrade, which suddenly makes your chatbot sound confused. Users notice. Immediately.

A GPU pool hitting 80–90% memory may not crash outright, but inference latency spikes, your streaming responses slow, and suddenly your AI writing assistant feels “broken.”

A schema change in an upstream data service? Your RAG app may not break instantly… just give it a few hours. It will.

AI doesn’t fail loudly. AI fails sideways. And those sideways failures are the hardest to catch.

Why AI Failures Are Harder to Catch (and Harder to Respond To)

Traditional outages announce themselves loud and clear. A server crashes, a CPU spikes, a health check fails, there’s your culprit. AI outages are…let’s just say…sneakier. They start as subtle quality degradation long before anything technically “breaks”..

Most AI-first teams already use observability stacks that are pretty robust. GPU metrics, drift dashboards, data freshness checks, pipeline monitoring, vector DB health, you name it. Monitoring tools are great at telling you what’s happening. But they don’t guarantee that anyone will: see it, respond to it, or escalate it, own it, and fix it.

And that’s where things get messy, because with AI systems, a delay of even a few minutes can change the scale of the incident.

Monitoring Detects. OnPage Mobilizes.

Monitoring tools are fantastic at surfacing what’s happening inside an AI system. They’ll tell you when GPU memory is creeping up, when a pipeline is lagging, when retrieval is acting strange, or when your agent chain is suddenly behaving like it forgot how to reason. They’re excellent at raising a hand and saying, “Hey, something here looks off.”

But as every AI-first team eventually learns: detection is not the same as response.

It doesn’t matter how beautiful your dashboards are if the alert quietly slips into a Slack channel at 2 AM that nobody is watching. It doesn’t help if the only person who understands that one flaky embedding pipeline is asleep, offline, or buried under a mountain of other notifications. And it definitely doesn’t help when an issue is subtle enough that everyone assumes someone else is dealing with it.

This is the gap, the one almost no one talks about. The monitoring stack sees the issue. But who actually owns it in that moment?

That’s where OnPage shows up. It sits in the middle of that very messy space between “we know something’s wrong” and “someone is actively fixing it.” When an anomaly fires: drift, latency, GPU overload, a vector DB that suddenly can’t remember anything, OnPage makes sure the alert doesn’t just exist somewhere. It reaches the right on-call engineer directly, on the device they’re actually going to notice, and it keeps alerting until they acknowledge it.

And if that person doesn’t respond? OnPage doesn’t shrug. It escalates, automatically, gracefully, and with full context, so the on-call chain doesn’t break simply because someone was asleep or away from their keyboard.

In AI-first systems where issues compound fast, this simple, cost-effective difference is everything.

AI Helps, But It Can’t Replace Humans in On-Call

And yes, AI absolutely plays a growing role in incident support. It can prioritize alerts, predict which component might drift next, or even attempt a few self-healing maneuvers, such as, restarting something here, rebalancing GPU loads there, patching over a failing retrieval step to buy you a little time.

But even with all that automation, truth be told, there’s a ceiling. AI can’t take responsibility for an outage. It can’t decide when something crosses a business or regulatory threshold. It can’t coordinate across Data Engineering, MLOps, Infra, SRE, and Product. And it certainly can’t lead a multi-layer incident when your embeddings, GPUs, and gateway queues all throw their own tantrums simultaneously. That’s the moment you need a real person to step in, not another automation loop.

AI can assist the on-call engineer. But it cannot be the on-call engineer.

As AI-first systems become more complex and intertwined, having a dependable way to bring a human into the loop, quickly, clearly, and with traceability, becomes essential. I don’t see the idea of on-call going away this soon. It’s just evolving into something more sophisticated.

AI Outages Aren’t Quiet. Users Notice Immediately.

One thing that’s become glaringly obvious in AI-first products: users feel AI outages instantly. They don’t need logs or observability tools. The experience itself starts falling apart.

The chatbot suddenly sounds confused. The creative tool slows down or stops streaming. The agent workflow gets stuck midway. Recommendations become eerily irrelevant.

AI doesn’t fail politely in the background. It fails right in the user’s hands.

And because AI is increasingly the “front door” to many products, even a small quality dip looks like a major outage. This is why AI-first teams are shifting from “monitoring our stack” to “actively operating our AI systems.” Observability alone isn’t enough when you’re dealing with systems that degrade quietly and impact users loudly.

Reliability Now Belongs to Multiple Teams, Not One

AI-first companies don’t have neat organizational boundaries, either. When something breaks, it’s rarely isolated to one team. A pipeline issue might start in Data Engineering, get noticed by MLOps, escalate to SRE when latency spikes, and end up affecting the product team because users are now complaining in real time.

Everyone owns a piece of the puzzle, which also means everyone owns a piece of the failure. And when that’s the case, clarity matters. Who is actually responding? Who’s on-call? Who acknowledges the alert? Who escalates when the issue crosses team boundaries?

Without a clean operational model, incidents bounce from person to person with painful inefficiency.

Where Traditional Alerting Breaks Down for AI-First Teams

Despite how advanced their infrastructure is, many AI-first teams are still managing incidents with alerting systems that weren’t built for the kinds of failures AI can generate. Slack pings get buried. Emails get ignored. Dashboards light up, but only if someone is actively staring at them. And internal scripts… well, they work right up until the moment they don’t.

It’s not that these approaches are bad. They simply weren’t designed for environments where quality degrades quietly, multiple teams own different layers of the stack, and the user impact shows up long before anything technically “breaks.”

AI-first outages need something more deliberate: a way to route alerts to the right engineering team, ensure someone actually acknowledges them, handle after-hours reliability without guesswork, escalate automatically when needed, and give teams a clear paper trail when they’re doing their postmortem the next day. AI-first outages need a robust on-call management and incident alerting tool that has stood the test of times.

That’s the operational gap OnPage fills.