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So, you’ve identified a use case and introduced an agentic AI system into your environment. But once the system has been implemented, how do you know it’s delivering value?

This series aims to break down the technical terms into an understandable format. If you missed the first entry in this series, check out What Even Is… The Cloud? This time, we’re going to dive into Observability.

Adoption isn’t just about buying a tool and turning it on. It requires clarity of purpose, careful cost management, and a realistic view of how new systems will interact with legacy infrastructure.

In a previous post, we explored the vibrant communities supporting the open-source software (OSS) movement globally. What draws me to the world of OSS is that it’s driven by a sense of community and volunteerism, both values that resonate deeply with me.

If you’ve been running log management open-source for a while, you know how it is—popular stacks such as ELK, Grafana Loki, or Graylog give you the power and control you’re looking for, along with a generous side order of cluster babysitting and painful upgrades. Open-source log management can feel amazing right up until your storage costs explode, queries crawl, or the one person who really understands your setup hands in their notice.

When it comes to understanding application performance, most teams start with Application Performance Monitoring (APM), and for good reason. APM is essential for tracking the technical health of your applications: it monitors service availability, response times, and error rates, and helps teams dig deep into backend systems to understand what’s going on.

For SolarWinds, AI is about more than innovation for its own sake. It’s about empowering partners and customers to navigate complexity and reduce operational strain. We believe that every IT professional should be able to spend less time firefighting and more time driving strategy. Partners play a central role in making this possible, helping guide adoption, strategy, and transformation.

In the first part of this series, we established that successful AI adoption in database performance hinges on strong foundations. The most effective teams use a common framework for their database operations: monitor for a single, unified view; diagnose by cleaning signals with baselines and anomaly detection; optimize proactively to build durable resilience; and ensure these practices work everywhere across hybrid and multi-vendor environments.

Every profession has its jargon that can seem confusing at first, including technology. Often, the meaning of the words we use every day can be misinterpreted or misunderstood due to any number of reasons. Sometimes marketing conflates terms, or an incorrect understanding can be inferred from poor context. And, often, it’s just assumed knowledge.

In today’s fast-evolving digital landscape, organizations depend on complex distributed systems to support critical business transactions, services, and applications. Ensuring these systems operate flawlessly is essential, as errors can affect multiple components, impacting revenue, customer satisfaction, and brand reputation. Consequently, effective error monitoring, detection, and root cause analysis (RCA) have become vital business imperatives.