Why Automation and Augmentation Are the Real Path to SRE Autonomy

/ AI, Automation, DevOps, Infrastructure
Why Automation and Augmentation Are the Real Path to SRE Autonomy

The News:

Ciroos Co-founder and CPO Ananda Rajagopal published insights from SRECon25, where skepticism about AIOps hype met the reality of day-to-day toil faced by site reliability engineers (SREs). His perspective: full autopilot is an aspirational goal, but meaningful progress will come from a continuum of automation, augmentation, and eventually autonomy. The article outlines how AI can reduce toil, improve decision-making, and earn SRE trust through progressive adoption. Read the full article here.

Analysis

Why Autopilot Isn’t the Starting Line

Across the application development and operations market, teams are being pitched visions of fully autonomous infrastructure. The reality, as noted by me in my theCUBE Research, is that most enterprises are still balancing human oversight with automation. Developers and SREs encounter outdated CMDBs, tribal knowledge, and runbooks that rarely match real-world conditions. Jumping straight to autopilot ignores this reality. Instead, the industry is trending toward layered adoption: first automating well-defined tasks, then augmenting decision-making, and only then delegating end-to-end workflows.

This staged adoption reflects a broader industry pattern: AI isn’t displacing human operators outright. Instead, it is enhancing them by reducing toil and streamlining repeatable processes. Enterprises that chase autopilot prematurely often run into trust and reliability issues that erode developer confidence.

Automation and Augmentation as Practical Wins

Rajagopal’s framework maps directly onto current developer pain points. Automation tackles deterministic tasks such as log parsing, health checks, or trigger-based scaling. Augmentation addresses scenarios where humans know the when and how but need support with the what, surfacing insights from observability data, proposing root causes, or summarizing incidents.

These practical applications are already reshaping SRE workflows. For example, incident triage can be shortened when AI eliminates irrelevant causes and surfaces likely culprits. According to theCUBE Research, reducing mean time to resolution (MTTR) is now a top priority for operations teams, and augmentation provides an immediate lever without requiring full autonomy.

Lessons From the Automotive World

The article’s analogy to self-driving cars is instructive. Most modern drivers rely on features like cruise control, lane-keeping assist, or adaptive cruise control (forms of automation and augmentation) while full autonomy remains rare. Similarly, SRE teams are already adopting AI copilots for routine analysis, and these incremental gains stack up quickly. Autopilot may be the end state, but intermediate features are delivering value today and building the trust needed for broader adoption.

Developer Implications

For developers and SREs, the takeaway is to view AI adoption as a continuum. Automation reduces toil and builds efficiency, augmentation enhances human decision-making, and autopilot becomes viable once systems prove reliable. Developers should treat AI systems like new teammates: starting with limited read-only access, expanding as confidence grows, and only delegating full control once guardrails and trust are in place.

This approach ensures that progress is steady, contextual, and aligned with enterprise risk tolerance. It also prevents the fatigue that comes from chasing vendor hype while preserving the aspiration of fully autonomous operations.

Looking Ahead

The SRE market is evolving toward a hybrid model where humans remain in governance roles while AI systems handle repetitive work and provide enriched insights. Over time, as trust and observability improve, autopilot capabilities may see wider adoption.

Ciroos’s perspective underscores that the industry’s real progress will come from compound improvements in automation and augmentation. For developers, the future of SRE tooling is less about a leap into full autonomy and more about building systems that earn trust step by step, paving a realistic path toward autopilot.

Author

  • Paul Nashawaty

    Paul Nashawaty, Practice Leader and Lead Principal Analyst, specializes in application modernization across build, release and operations. With a wealth of expertise in digital transformation initiatives spanning front-end and back-end systems, he also possesses comprehensive knowledge of the underlying infrastructure ecosystem crucial for supporting modernization endeavors. With over 25 years of experience, Paul has a proven track record in implementing effective go-to-market strategies, including the identification of new market channels, the growth and cultivation of partner ecosystems, and the successful execution of strategic plans resulting in positive business outcomes for his clients.

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