LangChain Founder on Agent Engineering: Evals, Traces, and Custom Harnesses
hwchase17 · x · 2026-08-14
LangChain founder Harrison Chase dived into the core components of AI agents and engineering practices at a Sequoia Capital event.
- Three elements of an agent: Consists of a Harness, a Model, and Context. The further your use case is out of distribution from the model's training data, the more you need to customize the harness.
- Engineering challenges: When scaling a hybrid human + AI company, off-the-shelf tools often fail to meet the needs of compounding learning loops, requiring custom harness engineering.
- Evals and Traces: Emphasized the importance of using tools like LangSmith for evaluations and traces, which is a critical technical infrastructure for measuring and optimizing agent performance.
Related event: LangChain Founder Preaches 'Own Your Intelligence' at Sequoia(4 posts)→
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