Sandboxing, Agent Harnesses, and Agent Teamwork

Sandboxing, Agent Harnesses, and Agent Teamwork

Author: Demetrios June 19, 2026 Duration: 1:19:53

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


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 50

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