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How do always-on, proactive agents like @Bot work? Watch my lecture at Stanford CS146S: 2:35 How we got here 6:13 What changed in the models 10:06 Inside an always-on agent 18:07 The harness 27:26 Context engineering 34:26 Where this is going

Lee Robinson

297,129 subscribers

584,839 次观看 • 4 天前 •via X (Twitter)

教育科学技术

Anya Rossi• Live Now

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29 条评论

eric zakariasson 的头像

eric zakariasson4 天前

@bot huge

Pratham 的头像

Pratham4 天前

@bot Fire fit @leerob.

Ben Dicken 的头像

Ben Dicken4 天前

@bot Excellent talk.

serx · fireply.ai 的头像

serx · fireply.ai4 天前

@bot what changed in the models is mostly tool calling not breaking every third step

Pratham 的头像

Pratham4 天前

@bot DAMN! The goat has dropped.

Josh Larson 的头像

Josh Larson4 天前

@bot Stanford Lecturer!!! How cool

Alexander Abelt 的头像

Alexander Abelt4 天前

@bot Is it somewhere on YouTube?

El aprendiz 的头像

El aprendiz4 天前

@poteto @bot @bot requiero la transcripción de esta conferencia

glitchedge 的头像

glitchedge4 天前

@bot always-on is the easy part. knowing when not to ping you is the actual hard part

shub 的头像

shub4 天前

@bot let's go!!

Nishkarsh 的头像

Nishkarsh4 天前

@bot saving context engineering part for tonight

Abdullah  的头像

Abdullah 4 天前

@bot Been following your content for a long time Watched the whole thing the always-on agent setup (server + VM, sub-agents, and keeping the main context sparse) is clear and practical The context engineering section at 27 min is the one I’ll rewatch

Simon Lin 的头像

Simon Lin4 天前

@bot I'm turning this lecture into a breakdown site: chapters, diagrams, the key ideas from the harness and context engineering parts. Will drop the link right here and post it once it's live. Also, hope we get to see you give this talk at Columbia someday 🙌

Lúcia Mees 的头像

Lúcia Mees4 天前

@bot @bot watch this for me and give me the summary

Rafael Ibayev 的头像

Rafael Ibayev4 天前

@bot proactive is the hard part. knowing when not to interrupt seems harder than doing the task itself

Kristian Phoenix 的头像

Kristian Phoenix4 天前

@bot 💡

Jide Lambo 的头像

Jide Lambo4 天前

@bot A lot to learn here. As mentioned, @bot harness returns errors like “the computer is asleep. wake it with the shell. then retry.” Our system returned a bare not_found, which is how it failed sometimes. Every tool should return a next step instead and not just fail.

Theodore Rogers 的头像

Theodore Rogers4 天前

@bot Yea, will definitely watch this later. x

Blum 的头像

Blum4 天前

@bot Thanks for the timestamps, convenient to pull out just the segments of the video that are interesting

Yin Lai 的头像

Yin Lai4 天前

@parkersmith @bot @bot save this

Dominik 的头像

Dominik4 天前

@bot always-on agents die on what they keep between wakes, not on the model call. the harness chapter is the one worth stealing.

Ryan Rong 的头像

Ryan Rong4 天前

@bot 🔥

Rajat Kapoor 的头像

Rajat Kapoor4 天前

@bot skipping straight to 18:07 lol

Khushal 的头像

Khushal4 天前

@bot 45 minutes on always-on agents and mine can't stay on through lunch

Arthur Alston MD MBA 的头像

Arthur Alston MD MBA4 天前

@poteto @bot @bot make a note to watch this

Alain 的头像

Alain4 天前

@bot Bookmarked for a quiet hour! Looking forward to learn a thing or two 🚀

Fightlook 的头像

Fightlook4 天前

@parkersmith @bot Bless us with a Cursor and Grok Bot reset pls sir.

enrico 的头像

enrico4 天前

@bot Looking forward to watching this over a coffee

anish 的头像

anish4 天前

@bot saving this one for later. the context engineering part at 27 minutes is what i'll watch first

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102,967 次观看 • 2 个月前

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

Dan Shipper 📧

67,958 次观看 • 6 个月前

LangChain's CEO and CTO spent 43 minutes on stage laying out the harness, the lifecycle and the infrastructure behind agents that actually survive production: · 2:00 - Why building agents is different from building software · 6:34 - What Deep Agents is: the agent harness explained · 14:48 - LangSmith Sandboxes, announced live · 19:20 - The Agent Development Lifecycle, defined · 25:58 - Why agent trace payloads keep getting larger · 29:38 - The architecture of SmithDB · 34:00 - How Clay and Vanta run on SmithDB · 42:50 - Building the platform for the entire lifecycle The harness is only the first of three layers. The article below covers the other two. Watch this before you ship your next agent ↓

LangChain's CEO and CTO spent 43 minutes on stage laying out the harness, the lifecycle and the infrastructure behind agents that actually survive production: · 2:00 - Why building agents is different from building software · 6:34 - What Deep Agents is: the agent harness explained · 14:48 - LangSmith Sandboxes, announced live · 19:20 - The Agent Development Lifecycle, defined · 25:58 - Why agent trace payloads keep getting larger · 29:38 - The architecture of SmithDB · 34:00 - How Clay and Vanta run on SmithDB · 42:50 - Building the platform for the entire lifecycle The harness is only the first of three layers. The article below covers the other two. Watch this before you ship your next agent ↓

slash1s

14,757 次观看 • 2 个月前