Bio
Harrison Chase is the co-founder and CEO of LangChain and one of the operators most directly responsible for shaping how teams instrument, observe, and evaluate LLM-based agents in production. His writing focuses on the seams between agent runtime and agent learning — what gets logged, how feedback gets attached, how teams actually compound improvements over time rather than shipping a brittle prompt and walking away. The recurring through-line is that observability infrastructure that captures traces without binding them to feedback is logging dressed up as learning.
Operating themes
- Operating thesis: trace + feedback is the minimum learning unit. Either alone is not.
- Agent observability — trace capture, span correlation, feedback binding.
- LLM evals — promotion-as-PR, evaluator agents, drift detection.
- Frameworks-as-experience — design choices in LangChain / LangGraph reflect what teams actually fail at.
Cards
- A trace alone teaches nothing; learning requires feedback attached to the trace — A trace alone teaches nothing; learning requires feedback attached to the trace [Tier A]
Sources captured
- 2026-05-05 — Agent observability needs feedback to power learning (LangChain blog)