LangChain's Harrison Chase Shares Agent Building Lessons on Twenty Minute VC
💡 Excellent recap of @hwchase17 + @HarryStebbings' conversation on @twentyminutevc ⤵️
The model-plus-context-plus-harness decomposition gives teams a concrete decision framework for where to invest effort first, rather than defaulting to fine-tuning. Practitioners building or budgeting for agents get a tested sequence for cutting spend and a candid view that memory distillation is still the hardest unsolved piece. The claim that horizontal agents will call vertical agents also hints at where agent infrastructure investment is heading.
LangChain CEO Harrison Chase discussed agent engineering with Harry Stebbings on the Twenty Minute VC podcast, with a recap shared by LangChain's Sydney Runkle. Chase framed an agent as model + context + harness, arguing the highest-ROI starting point is optimising context, then the harness, then model weights. He described continual learning as a loop of running agents, collecting traces, aggregating learnings, fixing a layer and measuring. LangChain applied this cost-reduction pipeline (visibility, controls, then optimisation) to cut its own coding agent spend, and noted memory remains largely unsolved and open models currently serve only narrow tasks in agent loops.
💡 Excellent recap of @hwchase17 + @HarryStebbings' conversation on @twentyminutevc ⤵️
💡 Excellent recap of @hwchase17 + @HarryStebbings ' conversation on @twentyminutevc ⤵️ Sydney Runkle @sydneyrunkle "it's underrated how hard it is to build agents!" great convo w/ @hwchase17 and @HarryStebbings on @twentyminutevc an agent = model + context + harness, and you can improve an agent by tweaking any of the three. a few takeaways: 1. the highest roi thing you can do is start by optimizing context (then the harness, then the weights); this is where domain knowledge lives and that's incredibly valuable 2. the story of continual learning is: run an agent, collect traces, aggregate learnings, fix something, measure the fix, loop! the fix can land in any of the three layers. those fixes all look different but the cycle is the same. 3. memory is still massively unsolved, but also massively important. the hardest part is distilling learnings from traces which is incredibly domain specific. 4. when you build agents, you're probably worried about a few things: accuracy, cost, latency, and security. we've seen these themes confirmed year after year in our dev surveys 5. when we think about reducing cost, the pipeline looks like adding visibility into spend, putting controls in place, and only then pushing on agent optimizations. this is how we reduced our coding agent spend @LangChain 6. model routing, which is great for cost controls, is most effective in the harness (where the context needed for a routing decision lives) 7. open models are used for more narrow tasks right now (small parts of the agent loop OR for very specific domains w/ post-training), but as they improve they'll power more core agent loops 8. we're looking at a future with both horizontal agents and vertical agents. horizontal agents will use the vertical agents, and infra needs to change to support this load (agents can do background work at a rate that humans cannot). youtube.com/watch?v=1jDCPE… 🔗 View Quoted Tweet 💬 0 🔄 0 ❤️ 1 👀 965 ⚡
