Operations | Monitoring | ITSM | DevOps | Cloud

More than half of all connections to web services now originate from mobile devices. Mobile apps are no longer peripheral - they are central to how businesses engage customers, deliver services, and generate revenue. Despite this shift, many organizations still rely on observability tools that are fundamentally server-centric. These platforms are adept at monitoring backend health, but they often fail to capture what’s happening at the edge - on the mobile device itself.

Modern DevOps observability tools are excellent for monitoring system health, tracking distributed traces, and aggregating metrics. However, they lack the fidelity needed for full-text search across application traffic. While observability platforms excel at showing what happened and when, they often fall short when you need to find where a specific piece of data (like an email address, user ID, or transaction token) appears as it flows through your entire application stack.

In modern observability practices, distributed tracing has become table stakes. Most application performance monitoring (APM) platforms encourage an “instrument everything” approach: Deploy an SDK or agent, hook into every service call and capture every user interaction at scale. On paper, this sounds like complete visibility. In practice, it can turn into a costly firehose of data with diminishing returns.

AI-powered coding assistants have transformed how developers write software. Tools like Claude Code, OpenAI Codex, Gemini CLI, Qwen Code, and OpenCode have introduced what many call “vibe coding” — a new paradigm where users describe their intent and AI agents handle the implementation details. But as these tools become integral to development workflows, a critical question emerges: how do we understand what’s happening under the hood?

As IT organizations enter 2026, many of the assumptions around monitoring and observability have already been tested. Throughout 2025, infrastructure teams made it clear that visibility alone is not enough. Alerts without context, short data retention, and fragmented tools limited teams’ ability to explain behavior, validate changes, and plan with confidence. This article looks at what emerged from those experiences and how observability expectations continue to shift.

TLDR: AI coding assistants have sped up code delivery, but created a validation gap. Historic telemetry and static analysis cannot predict the behavior of unfamiliar, high-volume code. Lightrun’s Runtime Context MCP closes that gap, allowing AI assistants to verify behavior before it breaks, and resolve issues in real time.

IT operations are changing faster than most people realize, making autonomous IT a 2026 reality, not a distant vision. Your team monitors tens of thousands of metrics, ingests terabytes of logs, and generates thousands of alerts daily. And somehow, you still find out about outages from customers before you see them in your tools. That gap between having visibility and actually understanding what’s happening has become the central problem.

For the past several years, Coralogix customers have used the platform to ingest, process, and analyze large volumes of observability data without the presence of artificial barriers or unexpected constraints. This flexibility has enabled teams to experiment freely, evolve their architectures, and scale smoothly alongside their systems.

After a decade of dashboards, alerts, and ever-expanding telemetry pipelines, observability is changing. No longer just the domain of engineering, the most innovative organizations are extending observability to all areas of the business to better understand system behavior, emerging risks, and customer impact. At the same time, rising cloud costs and increasing complexity are forcing organizations to be more intentional about what they observe and why.

If you’ve ever stared at your observability bill and whispered “there’s no way this is real,” congratulations! Your instincts are working.