Source details
- Original source
- Harrison Chase
- Published
- 2026-07-21
- Primary topic
- AI Agents
Why it matters
Agent products, browser agents, autonomous workflows, operator systems, and orchestration tools. This item originated as a short-form social post, so the context blocks below help expand it into tools, models, and evaluation guides.
What happened
great post by jeff! a few thoughts I had as I read along, wrt what we're building at langchain: > design a compound learning machine as you use or deploy agents, they need to be learning! high level, i believe traces are the core of this - you need to see what happened, get feedback on it, and then update the system > own your context, rent your intelligence agree with this sentiment - own your "tacit and institutional knowledge", make it easy to swap out models. i think the phrasing will get confusing tho - what exactly is intelligence? @satyanadella had a great post here ( nitter.net/satyanadella/status/20… ) where he says a lot of things that agrees with this, but he also says stuff like "In consuming intelligence, you are creating intelligence... This is your particular intelligence". the nuance is there is general intelligence (eg the models, which you should rent) and then your company proprietary intelligence, which you should own. both could reasonably be called intelligence though, so its confusing > build your tools, buy infra buy the lowest common denominator infra, build specific things on top of it. at LC we try to focus on lowest common denominator infra (observability, deployment, sandboxes), but we can do better - we have some cool stuff coming in that regard > develop rubrics evals are important! > store and learn from production traces yup. was this article just an ad for langsmith all along? who knows! Jeff Huber (@jeffreyhuber) Article 12 factor companies In the spirit of 12-factor apps, and 12-factor agents - I propose 12-factor companies. This document is a living document - I’ll continue to update it. Organizations shape technology and technology — https://nitter.net/jeffreyhuber/status/2079266941366407380#m
What to do next
Move into automation and workflow tools next so you can evaluate whether the agent story is actionable or still mostly experimental.
great post by jeff! a few thoughts I had as I read along, wrt what we're building at langchain: > design a compound learning machine as you use or deploy agents, they need to be learning! high level, i believe traces are the core of this - you need to see what happened, get feedback on it, and then update the system > own your context, rent your intelligence agree with this sentiment - own your "tacit and institutional knowledge", make it easy to swap out models. i think the phrasing will get confusing tho - what exactly is intelligence? @satyanadella had a great post here ( nitter.net/satyanadella/status/20… ) where he says a lot of things that agrees with this, but he also says stuff like "In consuming intelligence, you are creating intelligence... This is your particular intelligence". the nuance is there is general intelligence (eg the models, which you should rent) and then your company proprietary intelligence, which you should own. both could reasonably be called intelligence though, so its confusing > build your tools, buy infra buy the lowest common denominator infra, build specific things on top of it. at LC we try to focus on lowest common denominator infra (observability, deployment, sandboxes), but we can do better - we have some cool stuff coming in that regard > develop rubrics evals are important! > store and learn from production traces yup. was this article just an ad for langsmith all along? who knows! Jeff Huber (@jeffreyhuber) Article 12 factor companies In the spirit of 12-factor apps, and 12-factor agents - I propose 12-factor companies. This document is a living document - I’ll continue to update it. Organizations shape technology and technology — https://nitter.net/jeffreyhuber/status/2079266941366407380#m
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