Why We Built AIM: Addressing the Biggest Misconceptions About Autonomous Infrastructure
By Jonathan Kelly, Founder & CEO of Avaron
For the past several months, I’ve had the opportunity to speak with CIOs, infrastructure architects, network engineers, investors, and technology partners about what we’re building at Avaron. Almost every conversation eventually reaches the same point. Someone pauses, looks at me, and says, “So you’re building another monitoring platform?”
It’s a fair question. Over the last decade, the infrastructure industry has become saturated with monitoring tools, observability platforms, dashboards, and alerting systems. Every year another company promises better visibility, more metrics, faster alerts, or a cleaner dashboard. These tools have become incredibly sophisticated, and many organizations have invested millions of dollars implementing them across their environments.
The reality is that observability is no longer the problem most enterprises are trying to solve.
Modern organizations already know when something goes wrong. They receive alerts from Datadog, Grafana, Splunk, Prometheus, New Relic, Dynatrace, and countless other platforms. They know a server has become unhealthy. They know latency has increased. They know a database is consuming too much memory. They know a Kubernetes pod has failed. In many environments, the challenge isn’t detecting problems anymore. The challenge is what happens after detection.
That distinction is exactly why we started building AIM.
AIM Is Not Another Observability Platform
One of the biggest misconceptions about Avaron is that we’re trying to compete with observability vendors. In reality, the opposite is true.
We have tremendous respect for the companies that built today’s observability ecosystem. Those platforms have transformed the way organizations operate infrastructure. They provide engineers with visibility that simply didn’t exist a decade ago, and they remain an essential part of every modern enterprise stack.
We have no interest in asking customers to replace those investments.
Instead, we see AIM as the layer that sits directly above them.
Think about how an infrastructure incident unfolds today. An observability platform collects telemetry from servers, networking equipment, storage arrays, virtual machines, containers, and cloud services. It correlates metrics, logs, and traces before determining that something has deviated from normal operating conditions. The platform generates an alert, opens an incident, or pages an engineer.
At that point, however, the automation often stops.
A person still has to acknowledge the alert, investigate the surrounding telemetry, determine the root cause, decide on an appropriate remediation, and then execute the corrective action. Even with world-class monitoring, much of the operational burden still falls on engineers.
That workflow has remained largely unchanged for years.
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At Avaron, we believe the industry has reached the point where artificial intelligence can responsibly take on much of that repetitive operational work.
Rather than replacing observability, AIM consumes the telemetry those platforms already generate. It analyzes the operational context, identifies the likely root cause, compares the situation against historical incidents and predefined policies, determines whether remediation is appropriate, and, when authorized, executes corrective actions automatically.
In other words, observability platforms answer the question, “What happened?”
AIM is designed to answer the question, “What should happen next?”
We believe that’s the natural evolution of enterprise infrastructure.
We’re Not Building AI to Replace Engineers
Another misconception I hear frequently is that autonomous infrastructure somehow means eliminating infrastructure teams.
Nothing could be further from our vision.
I’ve spent enough time working with infrastructure to know that the most valuable asset inside any data center isn’t the hardware. It’s the people responsible for keeping it running.
The experience of a senior network engineer, systems administrator, or site reliability engineer cannot simply be replaced by an AI model. These professionals understand architectural tradeoffs, business priorities, regulatory requirements, and operational nuance that extend far beyond what any automation platform can infer.
The problem isn’t that engineers exist.
The problem is that we ask highly skilled engineers to spend an extraordinary amount of time performing work that computers are increasingly capable of doing themselves.
Every day, engineers restart failed services, clear temporary storage, recycle containers, restart virtual machines, reapply configurations, investigate false positives, acknowledge alerts, and execute the same remediation procedures they performed last week or even yesterday. These activities are necessary, but they rarely create new business value. They consume time, contribute to alert fatigue, and prevent engineers from focusing on architecture, security, performance optimization, and innovation.
Our goal is not to replace engineers.
Our goal is to remove the repetitive operational burden that prevents engineers from doing their best work.
That’s a future we think is worth building.
Check us out and our beta that is releasing soon! Avaron.ai
