AI in production operations is not just about generating code or summarizing dashboards. In an Oct. 1, 2026, InfoQ roundtable, practitioners describe agents helping with instrumentation, support, alert triage, incident troubleshooting, and post-incident review. Their central warning: more automation makes verification, safe boundaries, and human accountability more important—not less.
What the InfoQ roundtable covers
In the recorded discussion, 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. They consider how AI can turn operational data into useful insight and what production engineering should look like when agents participate in operational decisions. Watch the InfoQ presentation and read its transcript.
The speakers offer practitioner perspectives, not a controlled comparison of tools or organizations. Their examples are useful as ideas to evaluate in a team’s own environment, not as universal performance claims.
Where panelists see AI helping in operations
The discussion spans several parts of the software lifecycle. Agents may help teams instrument systems, answer support questions, triage alerts, troubleshoot incidents, and examine operational evidence after an event. That broader scope matters: production operations includes understanding and acting on signals, not only producing code.
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Support and alert channels
Sooreddy says the clearest gains she has seen at Netflix come from agents serving as first responders in support and alert channels. She also describes a reduction in time to resolve incidents in her experience, but gives no numerical result. This is her account of work in her setting, not a measured promise for other teams.
Operational data beyond engineering
Levi describes operational data becoming useful to people outside engineering, including for business questions. This points to a possible shift in how teams use observability data: it can inform decisions beyond diagnosing system behavior. The panel does not compare platforms or establish a recommendation for a particular vendor.
Adoption claims need careful context
Levi says some early-adopter companies told his organization that more than 80% of their observability-platform adoption was agentic. The presentation supplies no sample, methodology, or independent validation for that claim, so it should not be read as an industry-wide adoption rate.
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Why agent-assisted production needs stronger controls
When agents write more code or take operational actions, teams need dependable ways to check the work and limit its impact. Sooreddy argues that sound software engineering practices become more important as agents contribute more. She recommends verification-first infrastructure, contracts, checkpoints, automated rollback, canary promotion, and making SLOs and metrics part of everyday development.
“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.”
— Sujana Sooreddy, engineering manager at Netflix
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These controls are familiar engineering disciplines applied consistently to agent-assisted work. A canary limits exposure while a change is evaluated; checkpoints and contracts make expected behavior explicit; rollback provides a recovery path when verification fails. SLOs and metrics help teams judge production impact against service expectations rather than relying on an agent’s confidence or a plausible explanation.
“Previously, the metrics, SLOs might have been an afterthought, but not anymore.”
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How to choose an autonomy level for each task
Autonomy should be defined per operational job rather than as one organization-wide setting. Hausenblas points to Google’s SRE autonomy levels—from manual execution to full autonomy—as a way to express how much authority a particular job should have. The panel does not prescribe one universally safe threshold.
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A practical way to apply that advice is to consider the task’s risk, reversibility, context quality, verification and rollback capacity, and need for human approval. This is a synthesis of the panel’s guidance, not a formal scoring system.
| Decision factor | Question to ask | What it means for autonomy |
|---|---|---|
| Task risk | Could a mistaken action affect customers, security, or business outcomes? | Higher impact calls for narrower permissions and more human oversight. |
| Reversibility | Can the action be undone quickly and reliably? | Hard-to-reverse actions warrant stronger approval and safeguards. |
| Context quality | Does the agent have the relevant service, incident, and operational context? | Missing or ambiguous context is a reason to pause or escalate. |
| Verification and rollback | Can the result be checked, canaried, and rolled back? | Autonomy is easier to bound when verification and recovery are ready. |
| Human approval | Does this decision carry business impact that requires an accountable person? | Keep explicit human approval where the consequences require it. |
Safe sandboxes, clear escalation paths, and human accountability are part of the operating design, not optional extras. Hausenblas summarizes the stance as “trust but verify.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to start if your team has no AI in observability
The panel offers two possible starting points, not a tested comparison of onboarding strategies. Hausenblas suggests a small greenfield environment, where existing dependencies are less likely to complicate an experiment. Levi suggests identifying repetitive, low-friction tasks and giving an agent the relevant work context so it can help surface automation candidates.
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- Choose a bounded task. Prefer a repetitive job with a clear input and expected output over broad authority to operate a production system.
- Provide relevant context. Include the service or incident details the agent needs, while keeping the task within a safe sandbox.
- Set the authority and escalation path. Specify what the agent may do, where it must stop, and who is responsible for consequential decisions.
- Verify before expanding. Use checkpoints, metrics, SLOs, canaries, and rollback where applicable; assess the result before granting more autonomy.
This approach makes the experiment about a defined operational problem and its controls, rather than adopting AI as an undifferentiated platform feature.
Further reading on SRE practice
For general production-engineering context, Google’s Site Reliability Engineering: How Google Runs Production Systems covers SRE principles and practices, including building, deploying, monitoring, and maintaining large software systems. Google also lists The Site Reliability Workbook and Building Secure & Reliable Systems on its SRE books page. These are general SRE resources, not AI observability manuals or recommendations from the panel.
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