Insight
Chase: Agent harnesses matter more for performance than underlying models
“I, the, so I don't know what happens, but I do know the harness is really, really important. Like, I think this is the thing that matters.”
Prediction Not checkable as stated
Chase: Basically all AI agents will write code
“You know, if agents never write any code, then okay, maybe they're not useful, but I think it's trending where Basically all agents will write code, so that's a very interesting piece, I think.”
Assertion Supported
Anthropic's Claude Code uses custom harness tools over model-level RL tools
“It doesn't actually use the tools that are RL into the model. So like anthropic models have some like file editing tools. They have a completely different set of tools in, in the actual harness.”
Disclosure
LangChain adds tools for AI agents to autonomously trigger context compaction
“One interesting thing there, actually, that we haven't yet released as of this recording, but will probably be released by the time it comes out, is we actually give the agent a tool to trigger its own compaction.”
Prediction Not checkable as stated
Harrison Chase: AI agents will evolve into synchronous interfaces orchestrating asynchronous sub-agents
“Like, I do think we'll get to a place where we have this kind of like synchronous conversational agent kicking off kind of like longer running asynchronous agents in the background.”
Insight
Chase: AI builder differentiation lies in domain knowledge, not harness infrastructure
“I think a lot of the differentiation is in like the instructions and the tools and the skills and that basically, yeah, knowledge of how to do a process that you encode into natural language and give the agent and then the tools and the skills that you let it …”
Insight
Chase: High reliability AI agents require structured, graph-like scaffolding workflows
“So people ended up building scaffolding around the models to make them do things in a more predictable and reliable way. And that's why we at link chain, we built lane graph, which was another framework really aimed at that kind of like. Graph like workflows a…”
Assertion Not checkable as stated
Chase: Major AI labs' RL focus on code makes coding agents perform best
“All the big model labs have been RL and code and bash and editing files into those models. And so that is the stuff that works the best.”
Assertion Not checkable as stated
Harrison Chase: Most current agent harnesses feature built-in sub-agents and skills
“Most harnesses today have sub agents built in. They have skills built in.”
Insight
Chase: Human standard operating procedures should guide AI agent system prompts
“If you have a standard operating procedure for how a human should do things like that, that should influence a lot of what the system prompt is.”
Insight
Harrison Chase: Modern AI agent harnesses don't strictly enforce execution plans
“And so now what most things do is they just have that plan in the text file, and the main agent can, like, use that to help guide its actions, but there's nothing that says I'm explicitly doing this step, or I'm explicitly doing another step.”
Insight
Harrison Chase: Inter-agent communication is the hardest part of working with AI agents
“Communication is the hardest part of life, by the way, it's the hardest part of startups, hardest part of relationships, hardest part of working with agents is getting them to communicate.”
Insight
Chase: Virtual file systems let LLMs manage their own context windows
“And the way that I think about a file system is it basically lets the LLM manage its own context window. So it can decide what to read from files.”
Insight
Chase: AI agent context compaction must retain recent messages for flow
“You want to keep around like the last N messages, let's say the last like 10 or so messages, because if you compact everything, it actually like throws it off completely.”
Prediction Not checkable as stated
Chase: AI agents will clearly be long-running and stateful
“So I think, like, pretty clearly agents will be long-running and stateful, and so I think we have a deployments product. I think a lot of the I think deployments products that let you build long-running stateful things will be kind of, like, interesting no mat…”
Assertion Not checkable as stated
Developers are buying Mac Minis as primitive physical sandboxes for AI agents
“That's why people are buying Mac minis as a, you know primitive way of sandboxing them and keeping them in a contained environment.”
Assertion Not checkable as stated
LangChain sees a 50-50 split in how developers deploy agent sandboxes
“In practice, we see people doing about fifty-fifty between each of these.”
Insight
Harrison Chase: AI evals and prompt optimization are closely tied, unlike memory
“I guess evals and prompt optimization are pretty closely tied, but like evals and memory are actually not at all tied.”
Disclosure
Chase: LangChain does not maintain a one-year product roadmap
“I don't think we have a one year roadmap.”
Disclosure
Chase: LangChain is debating building a separate harness for conversational agents
“We talk a bunch about this internally because we're debating whether we should build like a different type of agent harness for these types of agents.”
Disclosure
LangChain's Deep Agents offloads large tool outputs into virtual files
“Is if you call a tool and it comes back with like 60,000 tokens, we don't show that all to the LLM because that's a ton of tokens. Rather, we actually put that in a file and then say, hey, here are the first like thousand tokens. If you want to read the rest, …”
Assertion Supported
Manus, Claude Code, and Deep Research share four core architectural primitives
“We saw Manus, we saw Cloud Code, we saw Deep Research, they all had these four things and we were like, okay, that's pretty common, let's put it into a Python package and make it easy for people to build their own versions of that.”
Disclosure
Chase: DeepAgents enables agent learning by editing procedural memory files
“So one of the things that we do in deep agents is we represent those all as files. And so the agent can update those as they go along so it can learn things. And so when we say agents kind of like can learn with deep agents, what that really means is it can mo…”
