In a rapidly evolving technological landscape, the role of artificial intelligence (AI) is increasingly crucial for businesses. During a recent episode of the Analytics Insight Podcast, hosted by Priya Dialani, Yadi Narayana, the Field Chief Technology Officer for Asia Pacific and Japan at Datadog, emphasized the growing importance of AI observability as organizations integrate autonomous AI agents into their operations.
Narayana explained how businesses are shifting from traditional AI models to autonomous agents capable of making decisions and managing complex tasks. These agents are used across various sectors, including healthcare, for purposes such as customer support and supply chain optimization. As these AI systems grow more sophisticated and autonomous, the need for effective observability and control becomes paramount.
Datadog positions itself as a leader in observability and security, currently serving over 30,500 customers globally, including 62% of Fortune 100 companies. The firm provides a unified platform that allows businesses to monitor everything from user experience to application performance and cloud infrastructure. In the second quarter of 2025, Datadog reported a remarkable 28% year-over-year revenue growth, reaching US$827 million.
Narayana attributes the company’s success to an “engineering-led innovation” approach, with R&D investment being three times higher than that of competitors. India has emerged as a significant growth market for Datadog, expanding its operations from one employee to a team of over 100 professionals in Bengaluru. The company now serves more than 300 customers in the region and collaborates with over 30 partners.
Observability, Narayana stated, is no longer limited to basic server monitoring. He described it as a multi-faceted approach involving preventive, detective, and corrective visibility crucial for supporting large-scale digital ecosystems, particularly in dynamic markets like India. His role as a “connector” between customers and R&D teams focuses on ensuring that observability and security translate into real business outcomes.
The significance of observability extends beyond technical metrics. Narayana highlighted that every digital experience, from online payments to video streaming, relies heavily on speed and reliability. When observability can predict anomalies or prevent system downtimes, it translates into improved customer satisfaction and enhanced revenue.
AI agents provide organizations with a new level of operational efficiency. As Narayana elaborated, “In traditional setups, engineers might spend hours investigating outages by manually analyzing dashboards and alerts. But with AI-powered observability, the system can process billions of data points, identify root causes, and suggest fixes in minutes.” This capability allows autonomous AI agents to function as virtual teammates, automating routine tasks such as investigations and compliance checks.
As businesses integrate AI more deeply into their workflows, the definition of success will increasingly depend on effective observability. Narayana asserts that observability forms the data backbone for AI systems, ensuring that clean and contextual telemetry data generates accurate insights rather than amplifying irrelevant noise. With Datadog’s all-in-one platform, organizations can unlock their full potential in driving AI-enabled operations.
Narayana concludes that observability is not merely a technical function but a foundational element that enhances AI’s effectiveness. By treating observability as a vital business enabler, organizations can achieve measurable productivity and sustainable growth. With leaders like Datadog spearheading enterprise-scale observability, businesses are well-positioned to adopt AI systems that are transparent, accountable, and trustworthy. This shift is not just about technology; it represents a significant step towards enabling business transformation.
