Can You Trust Your AI SRE?

The adoption of AI in site reliability engineering is limited by one thing above all else: trust.

In many areas of software engineering, AI tools can be adopted gradually. Mistakes are caught in code review, tests, or staging environments. Reliability work is different: incorrect analysis or slow answers during an incident can have immediate, real-world consequences. There is no margin for error.

Because of this, trust in AI for reliability is a prerequisite, rather than something that develops over time. 

Trust in AI SRE systems breaks down into three tightly linked dimensions:

1. Accuracy

2. Latency

3. User experience

Failure in any one of these is enough to prevent adoption. Understanding how these three dimensions interact helps explain why AI has been slower to take hold in reliability than in other parts of engineering.

Why AI Works for Coding but Struggles in Reliability

Software reliability has always been a pain point for complex organizations, but AI-assisted coding has fundamentally changed the operating environment. Code is now written, modified, and deployed faster than ever—often by AI—into systems that are already large, distributed, and constantly evolving.

The key difference between coding and reliability is not intelligence, but constraints. In coding, correctness can be established through iteration. Code can be test-run, reviewed, rolled back, and refined without strict time pressure. Errors are cheap; feedback loops are forgiving. During an incident, none of this is true. Decisions must be made in real time, under pressure, with incomplete information and immediate consequences. There is no opportunity to try again.

The scale of context also diverges dramatically. Even the largest codebases measure context in gigabytes. In reliability, the relevant context spans live telemetry—logs, metrics, traces, events, and infrastructure state—often measured in terabytes or petabytes across multiple systems. Understanding failure requires navigating that data quickly and accurately, not just generating plausible explanations.

This asymmetry explains why approaches that work for coding agents do not transfer cleanly to site reliability. Reliability demands faster answers, deeper context, and a far higher standard of accuracy and trust than code generation ever requires.

Accuracy

Accuracy is the foundation of trust in AI SRE systems, but achieving it in real environments is difficult. Production telemetry is inherently fragmented: metrics, logs, traces, deployments, configuration changes, and infrastructure state live across different tools, teams, and schemas.

However, the core problem isn’t that telemetry is fragmented; it’s how AI systems respond to that fragmentation. Systems that attempt to compensate for missing or partial data through inference tend to produce confident but incorrect conclusions, quickly eroding trust in operational settings.

Trustworthy AI SRE systems are built to work despite fragmentation. They ingest signals from many sources, reconcile inconsistencies, and make uncertainty explicit when context is missing. Accuracy comes from reasoning carefully about what is known, what is correlated, and what remains unclear.

Accuracy also depends on understanding how a system is structured. Distributed systems can’t be reasoned about reliably without an explicit model of how components depend on one another. Service relationships, infrastructure hierarchies, and failure paths need to be represented in a form machines can reason over.

Trust in accuracy starts even earlier, with how a system is introduced into production. Tools that require new agents, sidecars, or runtime components increase operational risk and slow evaluation. Trust-aligned systems minimize invasiveness by fitting within existing cloud or SaaS boundaries, avoiding new executables in production, and operating with read-only access to observability and infrastructure data.

Latency

Even perfectly accurate analysis fails to matter if it arrives too late.

In reliability, insight is time-bound. For AI systems to be trusted during incidents, they must operate at the tempo of response. Meeting these constraints requires real-time data ingestion and persistent connections to production systems. It requires data access patterns designed for repeated, large-scale retrieval, not human-driven dashboards. When latency is too high, AI tools are perceived as analytical or reporting tools rather than operational ones—and that perception alone is enough to prevent adoption.

User Experience

User experience is the final dimension of trust, and it reinforces the other two.

During incidents, engineers live in tools like Slack, PagerDuty, and ServiceNow. AI systems that require context switching to separate dashboards are unlikely to be used when pressure is highest. Trust grows when AI fits naturally into the environments where decisions are already being made.

Reliability work is also inherently iterative. Engineers ask follow-up questions, refine assumptions, and introduce new context as they learn more about an incident. AI systems designed around single, one-off queries don’t reflect how real incidents unfold—and quickly lose credibility as a result.

Trust is the key to adoption

AI’s slow adoption in reliability isn’t a failure of ambition or imagination. It’s a reflection of the standards operational work demands.

Teams exploring AI for site reliability should evaluate solutions against these trust constraints carefully. Systems that cannot deliver accuracy under uncertainty, operate at incident tempo, and integrate naturally into existing workflows will struggle to earn adoption, regardless of model sophistication.