LangChain: Design Agent Scopes Around Human Feedback
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LangChain argues that when building goal-oriented agents, the scope should be chosen so that people can easily give high-quality feedback, because unwritten expert context is the hardest thing to supply. In its case study, the sales agent builder Actively AI replaced per-account agents with "motions" — agents built on Deep Agents that each own a job, such as outbounding across many accounts — which LangChain says made review and cross-task learning easier. The claim is a design recommendation from LangChain, not a measured result; the Actively AI example is presented as a case study rather than evidence of improved outcomes.
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- LangChain BlogGoal Engineering: Designing Agent Scopes for Better Human Feedback
LangChain argues that when designing goal-oriented agents, the scope should be chosen so that humans can easily give high-quality feedback, since unwritten expert context is the hardest thing to supply. Its case study of Actively AI shows the sales agent builder replaced per-account agents with "motions" — agents built on Deep Agents that each own a job like outbounding across many accounts — making review and cross-task learning easier.
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