Harrison Chase

3 ingredients for building reliable enterprise agents - Harrison Chase, LangChain/LangGraph

Jul 23, 2025 · 20:55

Harrison Chase, CEO of LangChain, argues that reliable enterprise agents depend on three ingredients: high value when right, high probability of success, and low cost when wrong. He says agents succeed by tackling high-value tasks like legal or research, and by doing more work through long-running processes. Reliability improves by blending deterministic workflows with agentic loops, as in LangGraph, and by using observability (LangSmith) to build trust. Cost is lowered by reversible actions (e.g., code diffs) and human-in-the-loop patterns like approvals and time travel. Chase predicts a future of ambient agents—event-triggered, human-supervised background agents—and notes code agents lead because code is verifiable and easily revertible.

Building Context-Aware Reasoning Applications with LangChain and LangSmith: Harrison Chase

Nov 1, 2023 · 18:54

Harrison Chase, CEO of LangChain, argues that building context-aware reasoning applications on LLMs requires treating the model as part of a larger system, and he outlines key approaches and challenges. He categorizes context provision as instruction prompting, few-shot examples, retrieval-augmented generation (RAG), and fine-tuning. For reasoning, he describes a spectrum from single LLM calls to chains, routers, agents with cycles, and autonomous agents like Auto-GPT. Chase then details engineering challenges: choosing the right cognitive architecture, data engineering for context, prompt engineering across multi-step systems, evaluation using LLM-assisted metrics and user feedback, and enabling collaboration between technical and non-technical team members. He emphasizes that the field remains early and that LangChain and LangSmith help prototype, debug, and iterate on these systems.