Authored by Ganesh Narasimhadevara, Director of Solutions Consulting, New Relic India
In February, an online brokerage platform with over 1.5 crore users faced an outage at the worst possible time. With markets surging following India-US trade negotiations, investors were unable to make transactions, hundreds of reports were logged, and many turned to social media to vent.
These kinds of outages show that reliability has become a key differentiator, especially when time and money are on the line. But maintaining stable digital experiences is not always straightforward. Modern systems generate a constant stream of alerts across services and infrastructure, which makes cutting through the noise to prioritise signals about more than just conventional monitoring capabilities.
Battling the sea of notifications
Businesses today have huge volumes of telemetry data flowing into their systems from multiple monitoring tools, each generating thousands or even millions of signals every minute. Many organisations in India rely on four different tools just to track system health and uptime. Instead of clarity, this creates a maze of disconnected signals where connecting the dots becomes difficult, causing engineers to experience alert fatigue without a clear starting point to understand what went wrong.
Alert policies add to existing challenges. In an attempt to capture every possible failure, teams often create alerts with too many triggering conditions. This leads to a rush of ambiguous notifications that lack enough context to find a resolution. In most cases, teams filter notifications based on experience or tribal knowledge rather than evidence, which means serious issues can slip by unnoticed and cause larger incidents.
Reacting to every alert isnโt a viable option, and the real challenge is in separating important signals from the noise. The more tools in place, the higher the chances of teams hitting a wall without clear insights to troubleshoot. This is why some teams often take 30-90 minutes to detect high-impact outages. This time gap is all it takes for customers to experience the problem, get frustrated, and lose trust in the business.
Not every ripple is a wave
To build reliable systems, businesses need a way to extract meaningful insight from large volumes of raw operational data. Many organisations in India are turning to intelligent observability to address fragmented visibility across tools. Nearly 80% now spend at least $1 million annually on observability and 78% say that the value of observability is equal to or greater than its cost. Instead of isolated datasets, AI-strengthened observability consolidates telemetry across the digital environment and filters out noise; reducing manual effort while improving clarity.
Rather than presenting every alert separately, the system groups related signals that point to abnormal behaviour and a higher likelihood of escalation. It surfaces context, suggests possible root causes, and helps teams understand the potential business impact. It means that organisations using AI-strengthened observability resolve incidents about 25% faster than those relying on conventional approaches.
Observability must and should enable AI teams to deploy innovations backed by autonomous AI observability agents that work around the clock to analyse systems, detect anomalies in real time, and suggest remediation steps.
These agents act as autonomous teammates, allowing SREs and operations teams to capture signals amid the noise. Their deployment doesnโt require strong engineering backup; it can be used right out of the box for accurate monitoring. Plus, it comes with enterprise-grade governance capabilities, such as role-based access and audit trails, which helps teams stay compliant with regulatory standards.
In outages, such systems would identify abnormal patterns early, connect related signals, and initiate remedial action before users experience disruption. The outcome is not only fewer visible failures but also reclaimed engineering time that teams can invest into improving the product rather than constantly firefighting incidents.
Customer trust creates brand loyalty
Reliability directly shapes how customers experience a product. Users may never see the infrastructure behind an application, but they immediately notice when it does not work. The difference may seem small, but it changes how customers trust. In competitive markets, the product that consistently works is often the one consumers are loyal to.
The difficulty is not that organisations lack monitoring. It is that systems now produce more signals than DevOps and SRE teams can realistically interpret. Small anomalies rarely stay small for long, and most often they travel across connected services until users notice them.
Intelligent observability changes the sequence of events, so that instead of reacting after an incident becomes visible, teams gain the ability to understand system behaviour continuously and intervene earlier. This way, engineers spend less time retracing failures and more time improving the system.
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