LogicMonitor has released The SRE Report 2026 through Catchpoint, its Internet Performance and Digital Experience Monitoring business. Now in its eighth year, the report draws on insights from over 400 site reliability, DevOps, and IT professionals worldwide and reveals a clear shift in how reliability is defined, measured, and valued in the AI era.
“This year’s SRE Report makes one thing unmistakably clear: reliability has crossed a point of no return,” said Mehdi Daoudi, GM of Catchpoint at LogicMonitor. “It’s no longer something you prove with uptime charts after the fact. Reliability today is defined by speed, by experience, and by whether the business can trust its digital systems to perform in moments that matter.”
The findings point to a clear inflection point. Reliability is no longer proven by uptime alone. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet.
Now in its eighth year, The SRE Report is widely regarded as an authentic voice of the reliability community, highlighting the trends and tensions shaping how digital services are built, measured, and trusted. The 2026 edition highlights growing alignment between engineers and leadership that performance degradations are as damaging as outages, while also exposing persistent gaps in how organizations connect reliability to business outcomes, AI confidence, and long-term resilience.
“As AI and distributed architectures become foundational, reliability can’t stop at the application layer,” said Dritan Suljoti, Catchpoint CTO at LogicMonitor. “The data shows teams are grappling with complexity across the Internet stack, and that’s exactly where modern observability and Internet Performance Monitoring must evolve to keep pace.”
Key findings from the report include:
- Slow is the new down, and now the default expectation: Nearly two-thirds of respondents say performance degradations are as serious as outages, reinforcing speed and experience as core reliability outcomes.
- Reliability is felt by users, but rarely measured by the business: Only 26% consistently measure whether performance improvements affect business metrics, such as revenue or NPS, revealing a persistent gap between what users feel and what organizations track.
- AI optimism is surging, while confidence in observing AI lags: 60% of respondents express optimism about AI in SRE, and more than half plan to deploy agentic AI systems in production within the next 12 months. While this represents more than double the confidence reported last year, teams report low confidence in monitoring AI reliability, underscoring the need for observability across internal systems and external dependencies.
- Toil remains high, even as AI adoption grows: Median toil is 34% of engineers’ time. While 49% report AI has reduced toil, others report no change or increased burden, showing uneven outcomes between leadership expectations and frontline realities.
- Resilience maturity remains uneven: Only 17% run chaos or resilience experiments regularly in production, and nearly half report low tolerance for planned failure, pointing to a widening divide between proactive resilience teams and reactive teams.
- Learning has become a reliability risk factor: Despite broad agreement that learning matters, just 6% report protected learning time, and most spend only 3–4 hours per month on upskilling, raising concerns about knowledge decay as systems become more AI-driven and Internet-dependent.
