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Monitoring AI workloads is a priority for 85% of Indian organisations, but 43% struggle to control AI costs and 46% cannot effectively measure AI workload metrics, according to Cisco’s Splunk State of Observability 2026 report. Based on a survey of 2,050 IT operations and engineering professionals across 10 countries, the report highlighted the growing use of AI agents in creating challenges around monitoring, financial predictability, and security as enterprises expand deployments. “Most companies will soon have far more agents at work than people,” Jeetu Patel, President and Chief Product Officer at Cisco, noted in a release. “Managing agents at scale requires reimagining both security and observability. Have our agents been compromised? Are they doing their job well? In the world of agentic AI, these questions are inseparable, and they're moving at machine speed.” The report found that 39% of Indian respondents lacked clear business outcomes tied to AI monitoring, compared with 54% globally. While 87% in India considered visibility into AI hallucinations important, 70% said their organisations were effective at monitoring them, against 58% globally. AI costs remain another challenge. While 43% of Indian respondents said they could not accurately control AI costs, the global figure stood at 55%. Unexpected billing spikes caused greater operational strain than high baseline costs for 54% of Indian respondents, compared with 60% globally. Globally, 57% said telemetry costs had exceeded the cost of the infrastructure being monitored. Security concerns are also driving closer collaboration between teams. About 81% of Indian respondents were concerned about the unintended consequences of AI agents, while 89% said monitoring AI workloads had increased collaboration between observability and security teams, compared with 81% globally. Additionally, 81% in India could accurately trace performance issues to security-related root causes, against 73% globally. Indian organisations reported stronger model-quality monitoring capabilities than their global counterparts, with 51% describing their practices as very effective, compared with 35% globally. The report identified three practices among organisations achieving stronger observability outcomes: using AI agents to fix and prevent issues; monitoring model quality, costs, usage and security; and connecting telemetry data to business impact. Among these leaders, 91% could accurately attribute AI costs, compared with 69% of non-leaders. Additionally, 54% reported at least $5 million in annual business revenue from observability, compared with 28% of non-leaders.
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