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Give the Agent a Fair Environment: Director of Software Engineering on LLM-Powered RCA

anjali@stackgen.com (Anjali) — Fri, 04 Sep 2026 06:50:17 GMT

This piece comes from the AWS Roundtable “Killing the 90-Minute War Room: Automated RCA” at AISRENext Bengaluru.

The Same Old Mix: Infosys's SVP on Why Agents Are No Different From Any Other Stack Decision

anjali@stackgen.com (Anjali) — Wed, 02 Sep 2026 06:25:40 GMT

This piece draws from a fireside chat and AMA session, "Failures, Surprises, Advice," at AISRENext Bengaluru. The panel had already been asked, and answered, a build-versus-buy question from the audience.

AI SRE for Teams Running Prometheus, Grafana, and Loki

neel@stackgen.com (Neel Shah) — Tue, 01 Sep 2026 13:59:35 GMT

It's 2 a.m. Alert manager fires twelve pages for what turns out to be one root cause. The on-call engineer opens Grafana to find the root-cause dashboard the one built for exactly this moment timing out, because the query it needs to run crosses a cardinality wall nobody budgeted for. By the time the dashboard loads, the RCA has already been done by hand, the slow way: kubectl logs, three tabs of PromQL, a Slack thread full of guesses.

Suggest First, Act Second: GreytHR's Senior Director of Engineering on Climbing the Trust Ladder

anjali@stackgen.com (Anjali) — Mon, 31 Aug 2026 05:32:06 GMT

This piece draws from "Deep-Dive 2: From Copilot to Autopilot, How Organizations Climb the Trust Ladder," a whole-room discussion at AISRENext Bengaluru moderated by Sudheer Bhat, Principal Engineer at InMobi.

Troubleshooting, Metrics, and Alerting in the AI Era: What's Changed for SREs

Aakash Dabrase — Wed, 26 Aug 2026 12:50:50 GMT

In the AI era, SRE troubleshooting, metrics, and alerting have changed in three concrete ways: new signal types (model latency, inference cost, output drift) that traditional dashboards weren't built to track, faster-changing systems (AI-generated infrastructure and autoscaling AI workloads) that break static thresholds and stale runbooks, and AI itself entering the toolchain as a root-cause-analysis and alert-triage layer. According to StackGen's analysis of 73 documented AI SRE adoption decisions, 58.9% of teams adopt AI-powered tooling primarily to reduce operational toil and alert noise, and 24.7% adopt it specifically to cut incident response time. Together, these two reasons account for 83.6% of why SRE teams are changing their observability stack in 2026.

The Harness Problem: Cleartax's CTO on What It Takes to Ship an Agent

anjali@stackgen.com (Anjali) — Mon, 24 Aug 2026 07:12:27 GMT

This piece draws from a fireside chat and AMA session, "Failures, Surprises, Advice," at AISRENext Bengaluru. Someone in the audience pushed the panel on a question that comes up whenever teams evaluate AI agents: with tools like Claude available, why not just build the agent yourselves instead of buying one?

The Two-Hour Audit: AIVar’s AI Architect on AI SRE Readiness

anjali@stackgen.com (Anjali) — Thu, 20 Aug 2026 07:00:00 GMT

The AI SRE Files is our series where we sit in on conversations with practitioners about AI in the SRE world and pull out one idea worth keeping.

Know Your Stack Before You Add AI: StackGen's Lead SRE on Where to Start

Aakash Dabrase — Thu, 13 Aug 2026 08:38:41 GMT

The AI SRE Files is our series where we sit in on conversations with practitioners about AI in the SRE world and pull out one idea worth keeping.

How We Debug Multi-Stage AI Agent Workflows

Sabith K Soopy — Mon, 10 Aug 2026 11:10:05 GMT

An incident investigation moves through a series of dependent decisions. You first establish when the problem started, identify the systems involved, check the initial hypothesis against metrics, logs, or traces, and then bring the evidence together into a likely root cause.

MCP Servers for Developers: 8 Benefits Transforming Developer Workflows in 2026

neel@stackgen.com (Neel Shah) — Thu, 06 Aug 2026 11:51:31 GMT

If you've spent the last year watching AI assistants hallucinate API calls, generate stale Terraform configs, or confidently suggest kubectl commands that break in staging — you're not alone. The gap between "AI that can write code" and "AI that can actually operate your systems" has been the defining frustration of this tooling era.