Getting Humans Out of the Way: How to Work with Teams of Agents

Getting Humans Out of the Way: How to Work with Teams of Agents

Author: Demetrios April 7, 2026 Duration: 50:30

Rob Ennals is the creator of Broomy, an open-source IDE designed for working effectively with many agents in parallel. He previously worked at Meta, Quora, Google Search, and Intel Research. He has a PhD in Computer Science from the University of Cambridge.

Getting Humans Out of the Way: How to Work with Teams of Agents // MLOps Podcast #368 with Rob Ennals, the Creator of Broomy


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// Abstract

Most people cripple coding agents by micromanaging them—reviewing every step and becoming the bottleneck.


The shift isn’t to better supervise agents, but to design systems where they work well on their own: parallelized, self-validating, and guided by strong processes.


Done right, you don’t lose control—you gain leverage. Like paving roads for cars, the real unlock is reshaping the environment so AI can move fast.


// Bio

Rob Ennals is the creator of Broomy, an open-source IDE designed for working effectively with many agents in parallel. He previously worked at Meta, Quora, Google Search, and Intel Research. He has a PhD in Computer Science from the University of Cambridge.


// Related Links

Website: https://robennals.org/

https://broomy.org/

https://learnai.robennals.org/ (not yet announced, but should be by the time of the podcast)


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Timestamps:

[00:00] Agent Optimization Strategies

[00:21] Visual Regression Explanation

[05:35] Automated QA for Videos

[13:05] Verification System Design

[19:48] Agent Selection Strategies

[30:48] Parallel Agent Management

[35:30] Containerization and Cost Estimation

[42:48] Shifting to Agent Orchestration

[50:10] Wrap up


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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