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

Agentic Conversations (formally mlops.community)
Podcast Episodes
Skills over MCP on the streets of Tokyo [not-audio_url] [/not-audio_url]

Duration: 32:30
Tool descriptions tell an agent what a tool does. They don't tell it how to use five tools together, in the right order, following your conventions. That gap is where this conversation lives.Filmed at AGNTCon + MCPCon in…
Walking Tokyo Talking Agent Protocols [not-audio_url] [/not-audio_url]

Duration: 28:49
Two people, a wrong turn into a back alley, a community garden, and about thirty minutes of arguing about protocols on the streets of Tokyo.The guest is Angie Jones, VP of Developer Experience at the Agentic AI Foundatio…
Why Cost Per Million Tokens Is A Useless KPI? [not-audio_url] [/not-audio_url]

Duration: 38:49
A year ago, Palo Alto Networks built dashboards to track AI spend. Today those dashboards are useless, and the team that built them thinks that's the whole story.Recorded at FinOps X in San Diego, this conversation bring…
The Five-Layer Cake Approach to Scaling AI Without Wasting Money [not-audio_url] [/not-audio_url]

Duration: 38:55
In this episode of Agentic Conversations, we sit down with Ambud Sharma, Principal Engineer at Pinterest, responsible for general technology efficiency, fresh off delivering a controversial keynote on AI infrastructure o…
The Winchester Mystery House Problem in AI Development [not-audio_url] [/not-audio_url]

Duration: 59:41
AI models are starting to act like appliances, locked into one narrow way of working, instead of the flexible infrastructure they used to be. Drew Breunig, an AI and data strategist working with the Overture Maps Foundat…
How Predictive Analytics Stops Budget Overruns Before They Happen? [not-audio_url] [/not-audio_url]

Duration: 29:23
Every engineer at Wayfair can now see, in real time, exactly what their code costs, and that's on purpose. Brent Eubanks, FinOps Architect at Wayfair, walks us through what happens when you stop treating AI spend as a fi…
How To Delegate To An Agent Like You Would An Employee? [not-audio_url] [/not-audio_url]

Duration: 55:15
OpenAI's Codex developer experience lead sits down with a former comedian turned ML engineering lead for a conversation about what happens when computer use agents stop being a novelty and start actually running your day…
Why Your AI Bill Will Double Before It Gets Better [not-audio_url] [/not-audio_url]

Duration: 32:24
In this episode, we're joined by Josh Collier, FinOps Lead at Superhuman (formerly Grammarly), to explore what it really costs to run AI at scale and why the rules of the game changed faster than anyone expected.We discu…
MCP Goes Stateless [not-audio_url] [/not-audio_url]

Duration: 52:27
David Soria Parra is an Engineering Lead at Anthropic and one of the core maintainers of the Model Context Protocol (MCP). We explore the biggest evolution of the protocol since its launch, and why MCP is becoming the fo…
AI Hype vs. Real Value [not-audio_url] [/not-audio_url]

Duration: 42:37
Manish Dasaur is a Managing Director at PwC with over 20 years in data and AI, having helped 100+ clients navigate AI disruption and extract real business value from data, AI, and agentic AI initiatives. In this episode,…