By Peter Marelas, Senior Director of Product Management, New Relic
2025 was a year of rapid AI adoption and automation across every industry. Organisations are building complex digital systems that are more interconnected than ever before, powered by data pipelines, machine learning models, and automated agents. As these systems expand, the need for better visibility into how they perform, interact, and evolve has become essential.
In 2026, observability will move from an engineering tool to a strategic enabler of digital trust, resilience, and competitive advantage. With such complex digital ecosystems, organisations will find that traditional monitoring is completely inadequate in this new era of AI. Technology and business leaders will need intelligent observability not just to see what’s happening, but to understand why it’s happening, and what to do next.
The observability graph becomes the new system of record
Traditional CMDBs (manually maintained configuration databases) were built for slower, more predictable environments and can’t keep up with today’s fast-changing, multi-cloud landscape. The next progression is a live observability graph built from telemetry data. Think of it as Google Maps for your infrastructure, a real-time view of how every service connects. This real time graph will replace current databases as the single source of truth, helping teams trace issues instantly and even debug how autonomous systems interact. As agentic AI and interconnected services expand, this graph becomes the foundation of trustworthy, self-healing digital ecosystems.
Autonomous AI agents will automate incident response
Today’s observability tools tell engineers what’s happening. In 2026 and beyond, observability platforms will go beyond dashboards and alerts; they will identify and action. The next wave of platforms will introduce autonomous AI agents capable of identifying issues, reasoning through causes, and initiating fixes in real time.
Human oversight will remain essential with new ‘critic agents’ that will validate proposed actions, manage ambiguity, and escalate complex issues to people when needed. Engineers will be able to focus on defining rules and boundaries that shape system behavior by teaching AI how to think, not just what to do. This shift will drastically reduce repetitive firefighting, allowing teams to focus on innovation, resilience and improved system performance.
Observability moves from a cost centre into a profit centre In Southeast Asia, where organisations lose a median US $165.5 million annually to high-impact outages, the incentive to treat observability as an investment––not insurance––is essential.
In 2026, observability will be recognised not as an operational cost, but as a tool for optimisation and ROI. By combining telemetry with cloud cost data, observability systems can automatically pinpoint inefficiencies and even test cost-saving changes safely using FinOps automation. Intelligent observability will directly reduce downtime, streamline cloud spend, and accelerate decision-making, turning visibility into measurable business value.
From Data to Intelligence: Building Trustworthy Systems
Across these shifts, one theme stands out: observability is evolving from collecting data to creating enterprise intelligence. Observability graphs will provide real time context, while AI agents enable action. Together, they will help enterprises understand not just what their systems are doing, but why. As regulations tighten and AI drives more decisions, organisations need transparency and traceability built in. In 2026, organisations that deeply embed intelligent observability into their infrastructure and AI workflows won’t just see problems faster, they’ll build systems that are resilient, intelligent, and transparent, ultimately providing businesses with a vital tool to compete in the era of autonomous enterprise.
