Reliability Is the New Bottleneck of Innovation

Ronak Desai
Ciroos
Learn more about Ciroos

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures.

Development speed is no longer the primary bottleneck of innovation. Reliability is.

A Seismic Shift: Complicated to Complex

For more than a decade, digital transformation focused on abstracting infrastructure and it worked. Engineering teams quickly gained speed, scalability, and flexibility. However, this came with a hidden cost: a fundamental shift in the nature of system complexity.

There's an important distinction worth highlighting. Complicated systems are understood by analyzing their individual parts (think about a car engine or a mechanical watch). A complex system, however, shows emergent behavior that can't be predicted by examining its components in isolation. Modern software systems have crossed that threshold. They're not just complicated, they're complex.

AWS research showcases how modern applications typically involve hundreds of microservices working and communicating together, creating processes and depending on a shared infrastructure. A small change in one system can trigger a chain reaction across the platform. 
This shift from complicated to complex deeply impacts how enterprises experience and respond to any sort of failure.

Failure Is the Norm

Failure used to be gradual and localized in traditional systems. An alert was triggered, an engineer investigated, and the problem was quickly contained. Failures are now sudden and invisible … until they aren't.

Why is this happening?

Factors include hidden service dependencies, retry loops that amplify failures rather than containing them, and external service degradations that lie outside an organization's control. Incident response now requires engineers to simultaneously reason across metrics, logs, traces, configuration changes, external dependencies and historical behavior — usually under immense time constraints and with incomplete information.

The financial stakes of any disruption could not be higher. The Uptime Institute's Annual Outage Analysis found 54% of outages cost organizations more than $100,000, and 16% exceed $1 million.

The October 2025 service disruption of Amazon DynamoDB US-EAST-1 showcases this. A rare event where the system's own automation capabilities caused the deletion of the DNS record for the regional DynamoDB endpoint, leaving it with no valid DNS record. This rippled across AWS provided services and impacted consumer platforms like Spotify, Uber, Delta and some of Amazon's products like Prime Video. While DNS functionality was restored relatively quickly, systems gradually recovered over the course of over 15 hours, costing an estimated $75 million per hour globally.

Observability alone is not enough. It's not about just knowing what's happening. It's about making sense of things quickly and solving them under pressure.

Reliability Is a Knowledge Problem

A group of senior engineers typically hold all of the cards. As knowledge workers, they have an understanding of things most do not: system architecture, past incidents and resolutions, and the small signs to look out for that typically precede an issue. Unfortunately, when failures occur, organizations rely on these workers to quickly connect the dots.

The problem?

A model like this creates systemic risk. In the event that engineers are unavailable, time to resolution is significantly slower. The debugging process quickly becomes trial and error, slowing recovery. Unfortunately, institutional knowledge isn't scaled across teams. Strictly relying on the knowledge held by a handful of SREs impacts productivity. In fact, McKinsey's research on developer productivity shows developers spend up to 40% of their time on operational "toil" (maintenance, debugging, and firefighting) rather than building.

Reliability isn't impacted by access to data. Instead, it's constrained by access to understanding.

AI SRE: Scaling With Humans

Traditional reliability models were designed for a simpler time. They just can't keep up with the needs and environments that organizations have. These reliability models were designed for a different era of system complexity.

AI Site Reliability Engineering (AI SRE) introduces a different model. Gone are the days of waiting for signals to be interpreted. AI SRE continuously analyzes, correlates, and interprets operational data across the entire system. Identifying patterns and root causes transforms incident response into a proactive process versus being a reactive one.

This is about giving human engineers superpowers. AI SRE helps close the gap between incidents and resolutions, by scaling the deep system understanding that only a handful of engineers typically possess. Every team member now has the necessary knowledge, making operational excellence spread across the organization rather than held by just a few key individuals.

Reliability at scale is a competitive advantage. Systems that fail less and recover faster allow teams to build more than firefight.

Innovation is no longer defined by how fast software is built. It's defined by whether it operates reliably. Systems are growing in complexity, which has outpaced what human teams can track, reason and resolve any failures. It's not about removing humans from the equation. It's about scaling what makes them effective. We need human-like reasoning at AI-scale.

Ronak Desai is CEO and Co-Founder of Ciroos

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Reliability Is the New Bottleneck of Innovation

Ronak Desai
Ciroos
Learn more about Ciroos

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures.

Development speed is no longer the primary bottleneck of innovation. Reliability is.

A Seismic Shift: Complicated to Complex

For more than a decade, digital transformation focused on abstracting infrastructure and it worked. Engineering teams quickly gained speed, scalability, and flexibility. However, this came with a hidden cost: a fundamental shift in the nature of system complexity.

There's an important distinction worth highlighting. Complicated systems are understood by analyzing their individual parts (think about a car engine or a mechanical watch). A complex system, however, shows emergent behavior that can't be predicted by examining its components in isolation. Modern software systems have crossed that threshold. They're not just complicated, they're complex.

AWS research showcases how modern applications typically involve hundreds of microservices working and communicating together, creating processes and depending on a shared infrastructure. A small change in one system can trigger a chain reaction across the platform. 
This shift from complicated to complex deeply impacts how enterprises experience and respond to any sort of failure.

Failure Is the Norm

Failure used to be gradual and localized in traditional systems. An alert was triggered, an engineer investigated, and the problem was quickly contained. Failures are now sudden and invisible … until they aren't.

Why is this happening?

Factors include hidden service dependencies, retry loops that amplify failures rather than containing them, and external service degradations that lie outside an organization's control. Incident response now requires engineers to simultaneously reason across metrics, logs, traces, configuration changes, external dependencies and historical behavior — usually under immense time constraints and with incomplete information.

The financial stakes of any disruption could not be higher. The Uptime Institute's Annual Outage Analysis found 54% of outages cost organizations more than $100,000, and 16% exceed $1 million.

The October 2025 service disruption of Amazon DynamoDB US-EAST-1 showcases this. A rare event where the system's own automation capabilities caused the deletion of the DNS record for the regional DynamoDB endpoint, leaving it with no valid DNS record. This rippled across AWS provided services and impacted consumer platforms like Spotify, Uber, Delta and some of Amazon's products like Prime Video. While DNS functionality was restored relatively quickly, systems gradually recovered over the course of over 15 hours, costing an estimated $75 million per hour globally.

Observability alone is not enough. It's not about just knowing what's happening. It's about making sense of things quickly and solving them under pressure.

Reliability Is a Knowledge Problem

A group of senior engineers typically hold all of the cards. As knowledge workers, they have an understanding of things most do not: system architecture, past incidents and resolutions, and the small signs to look out for that typically precede an issue. Unfortunately, when failures occur, organizations rely on these workers to quickly connect the dots.

The problem?

A model like this creates systemic risk. In the event that engineers are unavailable, time to resolution is significantly slower. The debugging process quickly becomes trial and error, slowing recovery. Unfortunately, institutional knowledge isn't scaled across teams. Strictly relying on the knowledge held by a handful of SREs impacts productivity. In fact, McKinsey's research on developer productivity shows developers spend up to 40% of their time on operational "toil" (maintenance, debugging, and firefighting) rather than building.

Reliability isn't impacted by access to data. Instead, it's constrained by access to understanding.

AI SRE: Scaling With Humans

Traditional reliability models were designed for a simpler time. They just can't keep up with the needs and environments that organizations have. These reliability models were designed for a different era of system complexity.

AI Site Reliability Engineering (AI SRE) introduces a different model. Gone are the days of waiting for signals to be interpreted. AI SRE continuously analyzes, correlates, and interprets operational data across the entire system. Identifying patterns and root causes transforms incident response into a proactive process versus being a reactive one.

This is about giving human engineers superpowers. AI SRE helps close the gap between incidents and resolutions, by scaling the deep system understanding that only a handful of engineers typically possess. Every team member now has the necessary knowledge, making operational excellence spread across the organization rather than held by just a few key individuals.

Reliability at scale is a competitive advantage. Systems that fail less and recover faster allow teams to build more than firefight.

Innovation is no longer defined by how fast software is built. It's defined by whether it operates reliably. Systems are growing in complexity, which has outpaced what human teams can track, reason and resolve any failures. It's not about removing humans from the equation. It's about scaling what makes them effective. We need human-like reasoning at AI-scale.

Ronak Desai is CEO and Co-Founder of Ciroos

Related Links

Hot Topics

Related Vendors

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

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Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

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Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

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While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

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For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

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The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

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Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...