About This Episode

Every company runs on software. When it breaks (and it always breaks), humans step in. They check code, scour dashboards, make educated guesses, and hope they fix it before customers notice.

That is the slow, expensive way. In this inaugural episode of The Root Cause, host Priyank Upadhyay sat down with Imran, a veteran engineering leader and Docker Captain. Imran has spent over a decade keeping massive systems running at places like GE Healthcare, Razorpay, and Firebolt. We lay out our worldview on why the traditional AIOps approach is fundamentally broken, why it had to exist in the first place, and what the industry is getting wrong about observability.

This isn't a product pitch or a founder origin story, it's a deep dive into the paradigm shift from passive data collection to active Site Reliability Intelligence (SRI).

Key Takeaways

• Why observability dashboards are creating alert fatigue instead of solving incidents.
• The fundamental flaws in current generation AI SRE tools.
• How the RubixKube Platform Intelligence Layer acts autonomously to figure out what's wrong, fix it, and learn from every incident.
• What the transition from "Show me the data" to "Fix the problem" looks like in production.

The Untouched Mountain of Incidents

Priyank started the conversation with a simple but revealing question: Across a decade of operating massive systems, how many incidents actually get a complete, thorough root cause analysis (RCA)?

Imran’s answer was honest: "Most of them go completely unattended."

In a high-growth environment, a team might face 700 to 800 issues or alerts a month. Because SRE and engineering teams have limited hours in the day, they naturally focus on the critical, burning issues that directly hurt the business. The rest? They get buried. They are logged, noted down in a Jira ticket or a Confluence document, and forgotten until they happen again.

This creates a mountain of technical debt in the form of unresolved, minor anomalies. It is a quiet tax on system reliability, waiting for a larger system change to turn those minor anomalies into a major outage.

MTTR has Changed its Form, Not its Difficulty

Historically, the industry has lived and died by Mean Time to Resolution (MTTR). In the pre-cloud, pre-SaaS era, finding a bug meant SSHing into a server and reading a single log file. Modern observability tools promised to make this easier by consolidating logs, metrics, and traces into a single pane of glass.

But as Imran pointed out, these tools did not actually solve the human problem. They simply changed the medium of the search.

Instead of searching a raw text file on a server, we are now searching inside complex, expensive dashboard UIs. An engineer still needs to know exactly where to look, what query syntax to write, and what specific time window to search. The cognitive load has not decreased. In fact, because our systems are now distributed across hundreds of microservices, the search space is larger and more confusing than ever before.

The Crisis of Signal versus Noise

This brings us to the core issue of modern SRE: separating the signal from the noise.

In an effort to avoid missing anything, many companies choose to log absolutely everything. This creates two immediate crises:

1. The Financial Cost

Some organizations find themselves paying almost as much for their third-party observability and logging vendors as they do for their actual cloud infrastructure. You should not have to choose between knowing what your system is doing and staying within your budget.

2. Alert Fatigue

When everything is monitored, everything alerts. SREs become desensitized to warnings because ninety-nine percent of them are false positives or non-actionable background noise. When a real, critical issue finally happens, it is easily missed in the flood of notifications.

True observability is not about collecting every single byte of data. It is about collecting the right data and having the context to understand it.

Can AI Save Us, or Will It Just Deploy Bugs Faster?

Naturally, the conversation turned to Artificial Intelligence. With the rise of Large Language Models (LLMs), there is a lot of talk about "AI SREs" that can autonomously manage, debug, and fix infrastructure.

Imran is highly optimistic about AI’s ability to handle the repetitive, boilerplate tasks of engineering. Writing deployment scripts, summarizing massive volumes of logs, and initial anomaly detection are perfect use cases.

However, Imran and Priyank both agree that we must be cautious about letting an AI autonomously change production infrastructure. If an AI agent has the power to fix a bug, it also has the power to destroy an entire cloud infrastructure in seconds.

To safely adopt AI in operations, Priyank suggested a smart strategy: the "observe-only" mode.

Before giving an AI agent write permissions or letting it run automated scripts, let it run in the background. Have it observe the system, make recommendations, and show you what it would have done. Only when the team gains 100% trust in the model's decision-making process should they start handing over the keys to the kingdom.

The Efficiency Myth of the Skeleton Crew

There is a growing business narrative that AI will allow companies to replace large engineering teams with a handful of people using LLM prompts. Imran warned against this line of thinking.

While AI can make an individual engineer more productive, it cannot replace the deep architectural understanding required to run complex systems. If you cut your team down to three people under the assumption that AI will do the rest, those three people will quickly burn out. Furthermore, your external API and token costs will skyrocket, wiping out any savings you thought you were getting on payroll.

The goal should be leverage, not replacement. Use AI to take the boring, manual work off your SREs' plates so they can focus on high-value tasks: like building guardrails, improving system architecture, and actually solving that mountain of unattended RCAs.

How to Stay Relevant in a Weekly Cycle

To wrap up the discussion, Imran offered a piece of advice for both seasoned industry leaders and students entering the field: Stay curious.

The landscape of AI and infrastructure is shifting almost every single week. A model released this Tuesday might be vastly more capable than the one released last month. You cannot afford to look away. Keep experimenting, stay close to open-source projects, and never lose the desire to understand how things work under the hood.

The tools we use to look at our systems will continue to evolve, but the fundamental discipline of SRE remains the same: it is about understanding your systems, asking the right questions, and keeping the human element at the center of your technology.

What does observability look like in your organization? Are you drowning in alerts, or have you found a way to quiet the noise?