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Ontologies, Evals & Deployed Intelligence: Building Real AI Agents - Mitchell Troyanovsky, co-founder of Basis 0:04: Intro /Meet Mitch Troyanovsky 2:20: What is an AI Agent, actually? 4:07: The Gap Between LLMs and Embodied Intelligence 5:25: Why Ontology is the Missing Piece of Agent Design 8:10: Evals, Evals, Evals...

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Matt Turck

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10,969 views • 8 months ago •via X (Twitter)

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NEW Greylock Change Agents: Evaluating Agents with Braintrust There's been a lot of discourse around evals recently, so it's timely to drop the recording of Change Agents with Ankur Goyal on the topic of how he thinks about Evaluating Agents. Timestamps: (0:00) - Intro (1:22) - Building Braintrust (5:19) - Evaluating agentic systems (11:04) - Experimentation with evals (13:18) - Adopting evals (17:09) - Evals and complex agents (21:19) - Custom scoring (25:02) - LLM as a judge (26:44) - Human-manual evaluation (30:07) - How the best in-class teams manage evals (33:57) - Future of evals for agents (37:38 ) - Audience Q&A
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NEW Greylock Change Agents: Evaluating Agents with Braintrust There's been a lot of discourse around evals recently, so it's timely to drop the recording of Change Agents with Ankur Goyal on the topic of how he thinks about Evaluating Agents. Timestamps: (0:00) - Intro (1:22) - Building Braintrust (5:19) - Evaluating agentic systems (11:04) - Experimentation with evals (13:18) - Adopting evals (17:09) - Evals and complex agents (21:19) - Custom scoring (25:02) - LLM as a judge (26:44) - Human-manual evaluation (30:07) - How the best in-class teams manage evals (33:57) - Future of evals for agents (37:38 ) - Audience Q&A

Corinne Marie Riley

12,743 views • 8 months ago

So many people talking about AI evals. Hamel Husain and Shreya Shankar show me how to actually build one. Live. Hamel and Shreya teach the world’s most popular course on evals, and have long been at the forefront of this emerging and important new skill for AI product builders. I'm so excited to finally have them on the podcast. Learn: 🔸 WTF are evals 🔸 A step-by-step live walkthrough of how to create an eval 🔸 Why evals have become so important 🔸 The debate between “vibes” and systematic evals 🔸 Code-based evals vs. LLM-as-judge 🔸 Why you only need 4-7 evals (not hundreds) 🔸 Building an LLM judge in Google Sheets 🔸 Why you always want to start with error analysis 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Fin — The #1 AI agent for customer service: 🏆 dscout — The UX platform to capture insights at every stage: from ideation to production: 🏆 Mercury — The art of simplified finances:
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Ian Cairns

250,866 views • 8 months ago

Evals are the new PRD. The companies building AI products that actually work are running 12.8 eval experiments per day. Here is the playbook with Ankur Goyal, Founder and CEO of Braintrust ($800M valuation, behind Vercel, Replit, Ramp, Zapier, Notion, Airtable): ⏱ 1:43 Why vibe checks stop scaling ⏱ 6:35 Evals are the new PRD ⏱ 8:45 The Claude Code evals controversy ⏱ 18:48 Building an eval live from zero ⏱ 29:51 Connecting Linear MCP and iterating ⏱ 39:12 Why you need evals that fail ⏱ 43:36 Offline vs online evals ⏱ 47:40 Three mistakes killing eval culture The core framework: every eval is exactly three things. A set of inputs your product needs to handle. A task that takes those inputs and generates outputs. A scoring function that produces a number between 0 and 1. We built one from scratch on camera. Score went from 0 to 0.75 in under 20 minutes.
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I’m proud of the evolution of AI agents CAILA — an AI agent redefining utility through weather intelligence and real-world applications.
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I’m proud of the evolution of AI agents CAILA — an AI agent redefining utility through weather intelligence and real-world applications.

Ivo Josipović

189,319 views • 11 months ago

I like to think of evals as something active, not passive -- it's a North Star that steers LLMs toward higher intelligence. Evals should drive your RL/SFT/post-training decisions. Internal evals at frontier labs make a huge difference -- and you can see it in how models behave differently (GPT seems better at one-shot tasks, Claude at multi-turn). If you want to learn more about building evals that actually improve your model in post-training, check out our AMD x DeeplearningAI course "Fine-tuning & RL for LLMs: Intro to Post-training" (content is free):
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