Show Notes
Shahram Anver is the Co-Founder and CEO of Cleric, the autonomous AI SRE that investigates and root-causes production issues like an experienced teammate — often in under two minutes. Before Cleric, Shahram led MLOps, DevOps, and FinOps platform engineering at Gojek, Southeast Asia's super-app. In this conversation, he breaks down why production operations never kept pace with AI-accelerated development, and why the real unlock for an AI SRE isn't faster triage — it's an agent that *learns* and compounds operational memory across your whole org.
In this episode: 🔧 The on-call problem — Why one broken service still drags ten engineers onto a call, and how AI changes that 🤖 What an AI SRE actually is — How Cleric investigates across your existing observability stack instead of adding another tool 🧠 Learning over MTTR — Why Shahram argues the value isn't alert triage, it's an agent that gets better every investigation 🪜 Ramping like a new engineer — Explore the environment, learn from the work, talk to the team 🔁 The investigate–measure–learn loop — Turning what worked on one incident into context for the next 🕸️ Knowledge graphs & operational memory — Mapping teams, clusters, and dependencies so insight from one team helps another ⚡ Under two minutes to root cause — What "fast" really requires in a live production environment 🚀 The road to autonomy — From assisted investigation toward self-healing infrastructure If you're an SRE, platform engineer, DevOps lead, or anyone building or buying AI agents for production, this one's for you.
🔗 Links & Resources Cleric: https://cleric.ai Shahram on LinkedIn: https://www.linkedin.com/in/shahramanver/ Willem Pienaar (Co-Founder/CTO): https://www.linkedin.com/in/willempienaar/ Cleric launches the first self-learning AI SRE: https://cleric.ai/blog/cleric-launches-the-first-self-learning-ai-sre MLOps Community: https://mlops.community Join the community: https://go.mlops.community/slack
⏱️ Timestamps [00:00] Tech Jargon Confusion [00:27] Harness vs Model [08:48] Model Evolution in Cleric [13:36] Sandboxing and Simulated Environments [20:40] Shifting AI Perceptions [24:10] Managing Humans vs Agents [31:32] Steering Parallel Agents [34:16] Human Decision Integration in Models [43:28] 80/20 Data Split [49:40] Becoming a Skill [53:35] 2027 Agent Autonomy [59:14] Agent Learning in Production [1:04:31] Software as Personal Capabilities [1:08:31] Vibe Coding vs Durability [1:18:23] Wrap up
#AISRE #SiteReliabilityEngineering #AIAgents
More from Agentic Conversations (formally mlops.community)
AI can write your code, choose its own tools, and book your next trip. But who checks that it actually did the job?
What happens when you give 200 engineers who have barely touched AI a coding assistant and a six-month deadline to rebuild their platform? Pepijn Senders, a principal engineer who previously led AI enablement at HelloFresh, has done exactly that, and he chose Sonnet over Opus on purpose.
An AI slips out of its sandbox. A chatbot won’t explain your cat’s stripes. And your next shopping spree might not involve you clicking anything.
What happens to the web when the ones browsing it are no longer people? Sarah Drasner, Area Tech Lead at Google, sits down with us ahead of her Agentic Web keynote at AGNTCon + MCPCon North America. She works through that question from both directions: rebuild the web so agents can use it properly, or build agents smart enough to handle the web as it already exists.
Everyone is racing to build one agent that can do everything. Justin Schroeder, co-founder of Standard Agents, thinks the future looks more like thousands of tiny ones. Ahead of his talk on domain-specific agents at AGNTCon + MCPCon North America, he explains why tightly scoped agents might be the smarter bet, even if it feels like a step back toward the single-purpose machine learning models of a few years ago.