Observability today may have surpassed what human teams alone can accomplish.
While it began as a best practice to maintain the performance of systems and software, today’s observability platforms are delivering new levels of intelligence and insights with AI to boost innovation, productivity, and operational efficiency.
Open source has powered the evolution of observability over the last two decades, and it is critical to how organisations develop standards, tools, and frameworks that are adaptable, scalable, and cost-effective to leverage emerging technologies.
Modern software complexity
The transition to microservices, containers, and other cloud-native technologies, along with the adoption of DevOps practices, has transformed how software is built, delivered, and maintained. Playing offence with software enables businesses to accelerate time to market, respond faster to emerging opportunities, and deliver differentiated user experiences.
However, the shift has increased the complexity of monitoring, troubleshooting, and maintaining software. Organisations have become increasingly weighed down by the high volume of tools required to manage a broader surface area — from distributed applications and manual processes to disconnected systems. Constantly switching between tools wastes time, drains energy, and increases the likelihood of errors and poor decisions.
New Relic’s 2024 Observability Forecast report found that organisations in Asia-Pacific were most likely to use more than five tools (55%, compared to 43% in Europe and 35% in the Americas). Paradoxically, instead of helping teams innovate faster and improve mean time to detect (MTTD) and mean time to resolution (MTTR), this piecemeal approach generates an onslaught of new problems: data silos and blind spots, lack of data correlation, licensing and cost friction, among others.
The cost — whether it’s brand reputation, lost revenue, or lowered operational efficiency — is too high to ignore.
An open ecosystem approach
Organisations in the region recognise that using multiple, siloed open source tools to monitor data creates a disconnected view of the truth and results in considerable toil when troubleshooting issues.
According to New Relic’s Observability Forecast, a notable driver for observability among APAC respondents was the integration of business apps like enterprise resource planning (ERP) and customer relationship management (CRM), into workflows.
By plugging different sources of data into a single platform, IT teams are able to gain invaluable native visibility into the entire system’s performance — in context — to understand what is really happening and resolve issues before they escalate.
An application-agnostic approach to observability enables software engineers to instrument, dashboard, and alert across their entire technology stack, regardless of technology or use case.
Delivering real intelligence as AI takes hold
The imperative to capitalise on the promise of AI adds another layer of complexity to observability. AI tools require IT teams to monitor complex data pipelines, model training and inference processes, and dynamic scaling based on real-time data.
Traditionally, observability involves gathering and analysing telemetry data — such as metrics, events, logs, and traces (MELT) — to understand not only what is happening within a system but also why. This deeper insight is essential for detecting and addressing issues in real time, ensuring that systems operate efficiently under varying conditions.
As AI technologies continue to advance, observability must extend beyond traditional MELT data to capture the specific behaviours and performance characteristics of AI components.
The volume of telemetry data sources will increase exponentially as AI adoption expands. To fully realise the benefits of AI, the future of observability will centre on an open ecosystem of agent-to-agent orchestrations connected via natural language APIs.
These agents will enable users to automate research and complex tasks, enhancing productivity. The system will also be capable of delivering intelligence in context, offering highly relevant, accurate responses and recommendations to support business decision-making.
Powered by machine learning, predictive analytics can examine trends in telemetry data to anticipate potential system failures or performance bottlenecks before they occur. By predicting these issues, teams can take proactive measures — such as scaling resources or modifying configurations — to maintain consistent system performance and reliability.
The new era of open, intelligent observability will drive organisations to unlock a superior level of insights and value. An observability platform that connects with best-of-breed technology will enable organisations to drive growth and developer velocity by integrating across workflows and enabling insights wherever customers operate.
The new era of open, intelligent observability will push organisations to unlock a higher level of insights and value. A platform that integrates with best-of-breed technologies will help organisations align observability with their workflows and enable insights wherever customers operate.
