SREs To AI Agents: Prove Yourself Before You Touch Production

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SREs To AI Agents: Prove Yourself Before You Touch Production

Published<br>wed 15 Jul 2026 // 03:15 UTC

Trusting an AI agent to summarize user complaints about downtime is one thing; trusting it to fix the problem unattended is something else entirely.<br>A survey of 696 experts that The Register ran with NeuBird AI in April 2026 found that 73 percent are not using AIOps at all, another 19 percent are in pilot, and only eight percent have it in production.

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Asked what is stopping them, 60 percent of respondents cited a lack of trust, by far the biggest issue, with concerns about ROI, security and data quality each registering at around 12 percent to 13 percent.<br>NeuBird AI's Production Ops Agent is designed to close that trust deficit. Rather than summarizing the alert queue, it continuously correlates metrics, logs, traces, infrastructure telemetry, deployment activity and dependency relationships, then runs investigations across that combined picture to suggest probable root causes and next actions. It also works a step upstream. Rather than bolting a faster responder onto a noisy alert queue, NeuBird AI fixes observability at its source: through agentic instrumentation it generates the right signals, so the alert is high-signal by design. As Martel puts it, the point is to fix observability at the source, not patch the output. "Pointing a homegrown agent at the existing queue doesn't solve that, because DIY on noise is still noise."<br>Field chief technology officer Francois Martel sat down with The Register to talk through what the survey found, and why the next phase of AIOps will look nothing like the dashboards engineers have stared at for a decade. He also has views on what must change before SRE teams will let agents near their production systems.<br>Lots Of Interest, Very Little Deployment

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The data confirmed what Martel was already hearing in the field. "There's a lot of interest, but not a lot of action," he says. The pattern is familiar across agentic workloads: the categories that have taken off are the ones that come with an obvious human in the loop and an obvious verification path, such as coding agents and content generation. Operations is harder, because the work happens inside the running environment, on data the engineer hasn't seen yet, with consequences that show up in customer-facing systems.<br>He saw the same gap inside enterprises long before he joined NeuBird: a backlog of 300 candidate AI fixes and a flurry of early enthusiasm, followed by a year of slog before the first one shipped.<br>Part of that delay comes down to the speed of market development, since waiting six months for the tools to catch up with your expectations is sometimes the right call.<br>Another part of it is the wrong choice of tool category, because general-purpose agents do not fit SRE problems.

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"There are specialized agents that can do a much better job," Martel says, "and address some of the concerns" of safety, security, guardrails and hallucinations. The tool also has to fit into the team's existing workflows.<br>Trust Is Built, Not Declared<br>Martel doesn't try to argue with the trust-heavy concerns the survey surfaced. "Working with AI is a trust-building exercise, and AI has to learn in order to gain trust," he says. "I would say that's kind of the killer feature for AI agents. If you can show that you're learning and getting better, then you can gain trust."<br>That's why explainability sits at the center of NeuBird AI's design rather than being grafted on for the security review. The platform records the reasoning behind every decision so an engineer can interrogate it the way they'd interrogate a colleague's incident report. "Whenever you have an agent, you want to be able to audit the decisions that were made, and understand the reasoning behind the decision," Martel says. Internally, NeuBird AI captures every reasoning step via Langfuse. Explainability is only half of it. The platform is also SOC 2 Type II certified, read-only, and stores nothing, so trust is built into the architecture, not just the reasoning. Externally, the harder problem is presentation: early versions of the system surfaced so much detail that users described it as a wall of text. The fix was to make the reasoning interrogable rather than dumped, so engineers can chat with the system's memory the way they'd query a more senior teammate.

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Context Is What Makes The Answer Credible<br>The same survey found that 59 percent of respondents require near-perfect accuracy before they'll adopt, while another three in every ten will tolerate around 80 percent accuracy. That bar is unforgiving, and Martel argues it can only be cleared with better context engineering, not bigger models.<br>"The key to accuracy is this sweet spot between just enough context so that you're not missing things,...