AI in production operations is more than generating code or summarizing dashboards. In an Oct. 1, 2026, recorded InfoQ roundtable, practitioners from Genesys, Netflix, and groundcover describe using AI across instrumentation, support, alert triage, incident troubleshooting, and post-incident learning. Their central caution: as agents take on more operational work, teams need stronger verification, task-specific limits, and clear human accountability—not fewer engineering controls.
What the InfoQ roundtable covers
Moderator Renato Losio speaks with Michael Hausenblas, introduced as a principal software engineer in the SRE team at Genesys; Sujana Sooreddy, an engineering manager at Netflix working on media systems and observability; and Noam Levi, field CTO and founding engineer at groundcover. The discussion asks where AI is useful in observability and what good production engineering means when agents participate in operational decisions. InfoQ’s presentation page and transcript are the primary source for the panel’s views.
This is a practitioner discussion, not a controlled evaluation or a comparison of products. The examples and benefits below should be understood as the speakers’ accounts of their own settings, not universal results.
Where panelists see AI helping in operations
The panel treats AI assistance as a lifecycle-wide possibility rather than a feature limited to code generation. The examples discussed include helping with instrumentation, answering support questions, triaging alerts, troubleshooting incidents, and reviewing operational evidence after an event.
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Support and alert channels
Sooreddy says the clearest gains she has seen at Netflix come from agents acting as first responders in support and alert channels. She describes a reduction in time to resolve incidents in her experience, but gives no measured figure. That account is useful as a concrete example of where assistance may matter; it is not evidence of a guaranteed reduction for other teams.
Making operational data useful to more people
Levi describes operational data becoming useful beyond engineering, including in response to business questions. The idea is that people outside the team responsible for a system may be able to ask questions of relevant operational context instead of relying on engineers to interpret every signal for them. The roundtable does not establish how broadly this works or quantify its effect.
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What the adoption figure does—and does not—mean
Levi says some early-adopter companies have told his organization that more than 80% of their observability-platform adoption is agentic. This is his report of what those companies said; the transcript gives no sample, methodology, or independent validation. It should not be read as an industry-wide adoption rate.
Why faster agent-assisted work raises the bar for engineering
More code and operational actions produced with agent help do not remove the need to establish that the result is safe. Sooreddy puts the point plainly: “More and more, when I see that agents are writing the code, it doesn’t move our responsibilities of really good software engineering practices, but it actually makes it even more important to double down on it.”
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In practice, her recommendations include verification-first infrastructure, contracts, checkpoints, automated rollback, canary promotion, and treating service-level objectives (SLOs) and metrics as part of everyday development. These controls create ways to detect a bad change, limit its effects, and recover, whether the change was proposed by a person or an agent.
Sooreddy also says, “Previously, the metrics, SLOs might have been an afterthought, but not anymore.” The operational implication is straightforward: if agents can generate or carry out work quickly, teams need observable measures and explicit expectations to judge whether that work is acceptable.
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How to set agent autonomy for operational tasks
Autonomy is not a single safe-or-unsafe switch for an organization. Hausenblas points to Google’s SRE autonomy levels, which range from manual execution to full autonomy, as a way to describe the intended level for a particular job. The panel does not prescribe one threshold that is safe for every team or task.
A practical way to apply the discussion is to choose an autonomy level task by task, using these considerations. This is a synthesis of the panel’s advice, not a formal scoring model:
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- Risk: How much user, service, or business impact could an incorrect action cause?
- Reversibility: Can the action be undone quickly and reliably?
- Context quality: Does the agent have the current, relevant information needed to act correctly?
- Verification and rollback: Can the result be checked automatically, and is there a dependable way to roll it back?
- Human approval: Does the task require a person to review or approve it before it takes effect?
Use more limited autonomy when the context is incomplete, the impact is consequential, or recovery is difficult. A bounded, reversible task with strong automated checks may permit more independence. In either case, define the escalation path and keep people accountable for decisions with business impact. Hausenblas’s shorthand is: “I think the overall gist or the thought that I want to implant here from the get-go is really this trust but verify.”
How to begin if your team has no AI in its observability workflow
The panel offers two starting suggestions, not a head-to-head trial. Hausenblas suggests experimenting in a small greenfield environment, where existing dependencies are less likely to overwhelm the effort. Levi suggests identifying repetitive, low-friction tasks and connecting the relevant work context so an agent can help discover candidate automations.
- Choose a bounded first task. Look for repetitive work with a clear expected result and limited consequences if it goes wrong.
- Give the agent relevant context. Make the operational information needed for the task available, rather than expecting useful actions from an isolated prompt.
- Set the permitted autonomy. Decide whether the agent may only suggest, may act after approval, or may act independently for this task.
- Build in verification and recovery. Define how the result will be checked, where checkpoints or canaries fit, and how to roll back an unsuccessful change.
- Define escalation and accountability. Specify when the agent must hand work to a person, especially when business impact or uncertainty exceeds the task’s limits.
- Use the outcome to refine the boundary. Review whether the task’s context, checks, and escalation rules were adequate before expanding autonomy to other work.
The useful first step is not to automate the most consequential operation. It is to find a task small enough to bound, but representative enough to reveal whether the team can supply context, verify outcomes, and recover safely.
Further reading on production engineering
For background on the underlying SRE practices, Google Research describes Site Reliability Engineering: How Google Runs Production Systems as covering the principles and practices of building, deploying, monitoring, and maintaining large software systems; its publication record lists O’Reilly (2016). Google’s SRE books page also lists The Site Reliability Workbook and Building Secure & Reliable Systems. These are general SRE resources, not AI observability manuals or endorsements from the InfoQ panel.
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