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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...

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Matt Turck

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Mario Nawfal

338,265 Aufrufe • vor 1 Jahr

chat, is starknet gaming cooked? for episode two, loaf has joined me to talk about ai agents in gaming, eliza and Daydreams: Taskmarket . timestamps loaf introduction 00:46 what are ai agents? 01:16 what will gaming look like with ai agents? 02:02 whats the point of gaming with ai agents? 04:06 why is onchain gaming the ideal playground for AI agents? 05:11 is a gaming revolution imminent? 07:00 eliza & daydreams 08:36 endgame of ai agents and onchain games 13:33 when and where to play using agents 16:58 final remarks 18:47
19:44

chat, is starknet gaming cooked? for episode two, loaf has joined me to talk about ai agents in gaming, eliza and Daydreams: Taskmarket . timestamps loaf introduction 00:46 what are ai agents? 01:16 what will gaming look like with ai agents? 02:02 whats the point of gaming with ai agents? 04:06 why is onchain gaming the ideal playground for AI agents? 05:11 is a gaming revolution imminent? 07:00 eliza & daydreams 08:36 endgame of ai agents and onchain games 13:33 when and where to play using agents 16:58 final remarks 18:47

shadowfax

37,582 Aufrufe • vor 1 Jahr

An agent is three things: a harness, a model, and context. If you're serious about owning your intelligence, you probably want to own all three. LangChain founder Harrison Chase joined us at our Sequoia Capital Own Your Intelligence to talk about the piece that often gets the least attention: the harness. He offers a clear heuristic for when to build your own. The more out of distribution you are from what the models were trained on, the more you'll want to customize. And good technical content on how to actually measure performance with evals and langsmith. 00:00 Introduction 00:58 The three parts of an agent: harness, model, context 02:12 What a harness actually does 03:25 Customizing the core loop with middleware 04:41 Sandboxes, file systems, sub-agents, summarization 05:47 Cognitive architectures — and when you still need them 07:03 Build your own harness or use off the shelf? 08:24 In-distribution vs. out-of-distribution: the file-editing example 09:39 Why evals define what "good" means in an organization 11:04 Harbor: what an eval task actually looks like 12:11 Comparing harnesses and models on accuracy, latency, and cost 13:20 Why observability is underrated — it's usually the context 14:34 The data flywheel: traces → curation → experiments 15:42 Getting feedback through UX design and online evaluators 16:51 Demo: LangSmith Engine 19:23 Q&A: Running Engine on Engine, and "codex-ification" 20:44 Q&A: Will harnesses converge or diverge?
23:56

An agent is three things: a harness, a model, and context. If you're serious about owning your intelligence, you probably want to own all three. LangChain founder Harrison Chase joined us at our Sequoia Capital Own Your Intelligence to talk about the piece that often gets the least attention: the harness. He offers a clear heuristic for when to build your own. The more out of distribution you are from what the models were trained on, the more you'll want to customize. And good technical content on how to actually measure performance with evals and langsmith. 00:00 Introduction 00:58 The three parts of an agent: harness, model, context 02:12 What a harness actually does 03:25 Customizing the core loop with middleware 04:41 Sandboxes, file systems, sub-agents, summarization 05:47 Cognitive architectures — and when you still need them 07:03 Build your own harness or use off the shelf? 08:24 In-distribution vs. out-of-distribution: the file-editing example 09:39 Why evals define what "good" means in an organization 11:04 Harbor: what an eval task actually looks like 12:11 Comparing harnesses and models on accuracy, latency, and cost 13:20 Why observability is underrated — it's usually the context 14:34 The data flywheel: traces → curation → experiments 15:42 Getting feedback through UX design and online evaluators 16:51 Demo: LangSmith Engine 19:23 Q&A: Running Engine on Engine, and "codex-ification" 20:44 Q&A: Will harnesses converge or diverge?

Sonya Huang 🐥

75,702 Aufrufe • vor 18 Tagen

Datadog CISO Emilio Escobar on Securing Agents at Scale In this conversation, Emilio Escobar joins a16z's Joel de la Garza at Black Hat to cover what happens when you hand coding agents to 4,000 engineers, why the permissioning that worked for a decade broke the moment agents could write their own SQL, and why Emilio isn't panicking: 00:00 Intro 00:55 Securing agents 03:11 AI flattens the org chart 05:20 Role-based MCP servers & sandboxing agent credentials 07:34 Understanding agents' intent 10:27 How to avoid reward hacking 12:07 How the CISO's role is changing 14:05 "Security engineers will become real engineers" 18:12 Why Emilio isn't panicking Emilio Escobar Datadog, Inc.
22:12

Datadog CISO Emilio Escobar on Securing Agents at Scale In this conversation, Emilio Escobar joins a16z's Joel de la Garza at Black Hat to cover what happens when you hand coding agents to 4,000 engineers, why the permissioning that worked for a decade broke the moment agents could write their own SQL, and why Emilio isn't panicking: 00:00 Intro 00:55 Securing agents 03:11 AI flattens the org chart 05:20 Role-based MCP servers & sandboxing agent credentials 07:34 Understanding agents' intent 10:27 How to avoid reward hacking 12:07 How the CISO's role is changing 14:05 "Security engineers will become real engineers" 18:12 Why Emilio isn't panicking Emilio Escobar Datadog, Inc.

a16z

47,472 Aufrufe • vor 19 Tagen