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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

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578,966 görüntüleme • 4 gün önce •via X (Twitter)

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29 Yorum

eric zakariasson profil fotoğrafı

eric zakariasson4 gün önce

@bot huge

Pratham profil fotoğrafı

Pratham4 gün önce

@bot Fire fit @leerob.

Ben Dicken profil fotoğrafı

Ben Dicken4 gün önce

@bot Excellent talk.

serx · fireply.ai profil fotoğrafı

serx · fireply.ai4 gün önce

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

Pratham profil fotoğrafı

Pratham4 gün önce

@bot DAMN! The goat has dropped.

Josh Larson profil fotoğrafı

Josh Larson4 gün önce

@bot Stanford Lecturer!!! How cool

Alexander Abelt profil fotoğrafı

Alexander Abelt4 gün önce

@bot Is it somewhere on YouTube?

El aprendiz profil fotoğrafı

El aprendiz4 gün önce

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

glitchedge profil fotoğrafı

glitchedge4 gün önce

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

shub profil fotoğrafı

shub4 gün önce

@bot let's go!!

Nishkarsh profil fotoğrafı

Nishkarsh4 gün önce

@bot saving context engineering part for tonight

Abdullah  profil fotoğrafı

Abdullah 4 gün önce

@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 profil fotoğrafı

Simon Lin4 gün önce

@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 profil fotoğrafı

Lúcia Mees4 gün önce

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

Rafael Ibayev profil fotoğrafı

Rafael Ibayev4 gün önce

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

Kristian Phoenix profil fotoğrafı

Kristian Phoenix4 gün önce

@bot 💡

Jide Lambo profil fotoğrafı

Jide Lambo4 gün önce

@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 profil fotoğrafı

Theodore Rogers4 gün önce

@bot Yea, will definitely watch this later. x

Blum profil fotoğrafı

Blum4 gün önce

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

Yin Lai profil fotoğrafı

Yin Lai4 gün önce

@parkersmith @bot @bot save this

Dominik profil fotoğrafı

Dominik4 gün önce

@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 profil fotoğrafı

Ryan Rong4 gün önce

@bot 🔥

Rajat Kapoor profil fotoğrafı

Rajat Kapoor4 gün önce

@bot skipping straight to 18:07 lol

Khushal profil fotoğrafı

Khushal4 gün önce

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

Arthur Alston MD MBA profil fotoğrafı

Arthur Alston MD MBA4 gün önce

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

Alain profil fotoğrafı

Alain4 gün önce

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

Fightlook profil fotoğrafı

Fightlook4 gün önce

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

enrico profil fotoğrafı

enrico4 gün önce

@bot Looking forward to watching this over a coffee

anish profil fotoğrafı

anish4 gün önce

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

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Lunar

102,967 görüntüleme • 2 ay önce

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 görüntüleme • 6 ay önce

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 görüntüleme • 2 ay önce