Data silos are the natural result of decentralized systems and tooling decisions that optimize for individual departments rather than the organization as a whole. Common entities like "client," "customer," or "user ID" often differ across departments, complicating data integration -- custom ETL (extract, transform, load) processes (read: spaghetti code) that are challenging to scale and maintain. It doesn't have to be that way.
A while back, I gave a talk for the Cloud Native Compute Foundation (CNCF) on behalf of my previous company. It's long been a topic of fascination. Demand continues to surge for smarter monitoring, faster root cause analysis, and real-time automation to keep systems running smoothly. Further efforts I made on how AI enhances IT operations were later published externally here.
Since then, AI has accelerated by leaps and bounds. The basic ideas have become even more important as AI itself places greater and greater demands on IT infrastructure. What's more, there have been some important new developments. So let me bring you up to date.
AIOps—Automating and Enhancing IT Operations
To set the stage, let’s revisit AIOps itself. At its core, AIOps is about applying machine learning and automation to IT operations, identifying and responding to issues as—or ideally, before—they happen. Traditional monitoring can get bogged down by sheer volume, where AIOps steps in, making sense of endless data streams, identifying anomalies, and predicting issues to reduce downtime.
AIOps effectively extends the capabilities of IT teams, allowing them to focus on strategic initiatives rather than constantly chasing fires. It combines data from across the IT environment and distills actionable insights, managing the “noise” that can overwhelm traditional systems. These abilities are essential, but the field is moving forward fast with new developments like agentic systems.
The Rise of Agentic Systems in IT
Agentic systems represent a next-level progression in AIOps, where AI tools aren’t just following commands but are built to adapt and make decisions autonomously. These systems are designed to operate with a sense of “agency”—they’re not merely reactive but can proactively manage issues and even suggest solutions without human prompts.
In practical terms, agentic systems allow IT teams to take a more hands-off approach. Imagine an IT setup where the AI can independently reroute traffic around a server showing early signs of failure or self-tune system performance based on demand spikes. This kind of autonomous action is incredibly powerful, yet it also brings challenges around trust and transparency, which is where explainable AI comes into play.
Explainable AI—Building Trust and Transparency
With AI making more decisions independently, there’s a growing need to understand how it’s making these choices. Explainable AI (XAI) is all about creating systems where we can see into the “why” behind AI decisions. For AIOps and agentic systems, this isn’t just a nice-to-have feature but a necessity.
For example, if an AIOps solution flags a server as vulnerable, explainable AI helps clarify why that call was made—whether it’s an unusual traffic pattern, memory use, or something else. This transparency is key to gaining user trust and enabling IT teams to effectively work with AI, rather than feeling blindsided by its choices.
Responsible AI—Ensuring Ethical and Safe AI Usage
Alongside transparency, responsible AI is about the ethical and safe use of these powerful technologies. It goes beyond performance—it’s about accountability, fairness, and reducing risks. In AIOps, responsible AI could mean ensuring that autonomous actions don’t unfairly favor certain system types, applications, or users. It’s also about ensuring data privacy and secure data handling, especially in sensitive industries like finance or healthcare.
A responsible AI approach helps establish guardrails for agentic systems, ensuring they function within ethical and regulatory standards while benefiting everyone involved. This responsible framework is becoming critical as more businesses adopt AIOps, ensuring that technology truly serves the people relying on it.
Conclusion: The Future of AIOps—Smart, Transparent, and Responsible
Reflecting on my original article, it’s clear that AIOps continues to be a game-changer for IT operations. But it’s also exciting to see how new ideas—agentic systems, explainable AI, and responsible AI—are enhancing its value even further. As these concepts evolve, they’re building a future where AIOps doesn’t just automate IT operations but does so in a way that’s transparent, trustworthy, and aligned with our ethical standards.
If you’re interested in learning more about how AIOps, agentic systems, and responsible AI can impact your organization, check out my original article here.
