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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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11,155 просмотров • 1 год назад •via X (Twitter)

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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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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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125,296 просмотров • 1 год назад

Before founding Vals AI, Rayan Krishnan in DC was a college radio host. We're thrilled to get him back in his element with Vivek Gopalan, this time talking all things benchmarks and evals. As AI capabilities compound, measuring them is becoming both harder and more important. What started as an academic exercise is quickly evolving into critical infrastructure for understanding what AI systems can actually do, and how they perform in the real world. Rayan and Vivek go deep on how benchmarks are evolving, measuring increasingly agentic systems, the weird and wonderful ways we test intelligence, the global AI race, and why evals sit on the critical path to ASI. 0:44 Importance and evolution of benchmarks 8:16 From academic artifact → real-world value 10:57 The next generation of benchmarks 13:34 Benchmarking models vs. agents 16:20 Building benchmarks for organizations 22:09 Weird and wonderful benchmarks 25:58 The AI arms race: US vs. China 31:02 Why evals are on the critical path to ASI 32:50 Vals Series A
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Before founding Vals AI, Rayan Krishnan in DC was a college radio host. We're thrilled to get him back in his element with Vivek Gopalan, this time talking all things benchmarks and evals. As AI capabilities compound, measuring them is becoming both harder and more important. What started as an academic exercise is quickly evolving into critical infrastructure for understanding what AI systems can actually do, and how they perform in the real world. Rayan and Vivek go deep on how benchmarks are evolving, measuring increasingly agentic systems, the weird and wonderful ways we test intelligence, the global AI race, and why evals sit on the critical path to ASI. 0:44 Importance and evolution of benchmarks 8:16 From academic artifact → real-world value 10:57 The next generation of benchmarks 13:34 Benchmarking models vs. agents 16:20 Building benchmarks for organizations 22:09 Weird and wonderful benchmarks 25:58 The AI arms race: US vs. China 31:02 Why evals are on the critical path to ASI 32:50 Vals Series A

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🎙️ New Deployed episode with Ryan Carson, "Builder in Residence" at Amp. It's a great convo about building code agents and building as a solo founder at the same time, and it's refreshingly honest. Case in point, on evals for coding agents: "We do not have a set of evals that we programmatically run to decide if GPT-5 is better than Sonnet-4 yet... everybody in the trenches building these agentic coding tools understands that it is so dynamic, and so frustrating." Great conversation about building with AI, developer experience, and what happens when everyone can code. Check it out. 👇
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🎙️ New Deployed episode with Ryan Carson, "Builder in Residence" at Amp. It's a great convo about building code agents and building as a solo founder at the same time, and it's refreshingly honest. Case in point, on evals for coding agents: "We do not have a set of evals that we programmatically run to decide if GPT-5 is better than Sonnet-4 yet... everybody in the trenches building these agentic coding tools understands that it is so dynamic, and so frustrating." Great conversation about building with AI, developer experience, and what happens when everyone can code. Check it out. 👇

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New session with Shreya Shankar on How to Automate Evals with AI (correctly) The most important part of the eval workflow is finding issues. Shreya does a live demo of how to steer the AI iteratively to find unknown unknowns Chapter summaries: 00:00:00 Introduction 00:00:55 Why vendors want to automate evals for you 00:01:45 Why no tool can fully automate evals 00:06:00 Mistake #1: Asking AI to just "find the issues" in your data 00:08:11 What good AI-assisted error analysis actually looks like 00:11:12 Live demo: building a review interface from scratch 00:15:10 Annotating traces and building a failure mode taxonomy 00:19:31 Mistake #2: Only reviewing your data once 00:22:53 Mistake #3: Treating all apps with the same accuracy bar 00:25:10 General-purpose agents vs. dedicated eval tools 00:25:48 Summary: three rules for automating evals correctly YT links and more in reply

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34,707 просмотров • 4 месяцев назад

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Braintrust

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Paril Jain, co-founder and CTO of The Bot Company, and Quan Vuong, co-founder of Physical Intelligence met on the Runway AI Summit stage to talk real-world evals, deploying robots in the field and where robotics will be in three years.

Runway

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LangChain

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Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 Intro 02:13 What is an AI agent sandbox? 03:17 Security risks of running agents locally 05:17 Stateful vs. stateless hyperscalers 07:04 The history of cloud IDEs and the end of localhost 09:45 Do all AI agents need a sandbox? 12:26 Sandbox use cases: RL evals & background agents 14:10 Unpacking the emerging AI Agent Stack 16:20 The unsolved problem of agent memory and learning 19:37 Where sandboxes fit in the agent harness 21:35 OpenAI, Anthropic, and agent SDKs 23:06 Ivan's founder journey: From CodeAnywhere to Daytona 26:59 GTM strategies and building developer communities 33:48 Why customer support is your best GTM strategy 35:34 Leveraging Twitter during the AI super cycle 40:50 The technical anatomy of a sandbox 41:53 Why fast spin-up speeds maximize GPU efficiency 46:09 Firecracker, QEMU, and isolation primitives 49:58 Why sandbox snapshots and state forking matter 51:40 Why Daytona built a custom scheduler from scratch 55:24 The challenge of long-running stateful sandboxes 58:10 The build your own sandbox trap 1:01:03 Why AI agents might trigger a global CPU shortage 1:02:46 The future of the AI Agent Stack

Matt Turck

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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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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):

Sharon Zhou

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I got an expert to give me a masterclass on AI evals: (63 mins of stuff most builders learn the hard way) 0:00 - Why your product is lying to you 22:03 - Full eval flow walkthrough 35:42 - Evals ARE your PRD (not QA) 57:52 - Failures at Grammarly, Copilot & Klarna

Aakash Gupta

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This is EVERYTHING you need to know about building a Company Brain in 28min. From connecting all your tools into one intelligence layer to the new org chart around it. This is the new way to work in the AI era. Here's the full breakdown: 00:00 Introduction & AI Adoption 01:45 The Four Levels of AI Maturity 02:30 The Single Brain Concept 05:30 Building an AI-Powered Org 07:20 Specialist Agents & Agent Fleets 11:20 Workflows & Closed Loops 14:30 Real Agent Examples in Slack 18:00 AI ROI & Token Economics 20:45 Business-to-Agent Commerce 24:10 Action Steps & Future of Work

ericosiu

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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

189,607 просмотров • 1 год назад

The creator of Pydantic explained how to make live agents smarter after they ship in 1 hour 21 minutes - better than $2500 agent evals bootcamps. collect traces from your live agent -> score them with an eval -> run GEPA to auto-optimize the prompt -> deploy the winner -> repeat forever. That loop is how Pydantic AI agents self-improve while running in production. Pydantic AI + Logfire tracing + GEPA prompt optimizer + evals + feedback loops - that's the stack. Watch and save it, then wire GEPA into your live agent this week.
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The creator of Pydantic explained how to make live agents smarter after they ship in 1 hour 21 minutes - better than $2500 agent evals bootcamps. collect traces from your live agent -> score them with an eval -> run GEPA to auto-optimize the prompt -> deploy the winner -> repeat forever. That loop is how Pydantic AI agents self-improve while running in production. Pydantic AI + Logfire tracing + GEPA prompt optimizer + evals + feedback loops - that's the stack. Watch and save it, then wire GEPA into your live agent this week.

h100envy

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Insights Agent & Multi-turn Evals Agents run for a long period of time and have multiple interactions with users, tackling a wide variety of problems. Today we’re launching two new features in LangSmith, our agent engineering platform, so you can better understand your agent behavior: • Insights Agent is a new agent that automatically categorizes agent behavior patterns in LangSmith • Multi-turn Evals lets you evaluate agent trajectory across complete conversations, so you can see if the agent actually accomplished user goals Learn more:
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Insights Agent & Multi-turn Evals Agents run for a long period of time and have multiple interactions with users, tackling a wide variety of problems. Today we’re launching two new features in LangSmith, our agent engineering platform, so you can better understand your agent behavior: • Insights Agent is a new agent that automatically categorizes agent behavior patterns in LangSmith • Multi-turn Evals lets you evaluate agent trajectory across complete conversations, so you can see if the agent actually accomplished user goals Learn more:

LangChain

13,375 просмотров • 1 год назад

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.
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Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

Alex

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Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives
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Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives

Matt Turck

35,333 просмотров • 8 месяцев назад