Harrison Chase
Showing 1–5 of 5 transcripts.
- Sequoia Capital24 min
When to Build Your Own Agent Harness | Harrison Chase, LangChain
The framework defines autonomous agents as systems built from three owned components: the model, context, and a prioritized harness that orchestrates data flow through an iterative LLM loop. Organizations can customize this harness via middleware for domain-specific optimizations or maintain off-the-shelf versions for in-distribution tasks, ensuring compatibility through dynamic model profiles. Continuous improvement is driven by a flywheel where trace data from evaluations using the Harbor benchmark feeds into an automated engine that identifies failures and suggests prompt, code, or context fixes.
- Sequoia Capital40 min
Context Engineering Our Way to Long-Horizon Agents: LangChain’s Harrison Chase
Harrison Chase, Sonya Huang, Pat Grady
The shift from scaffolding to agent harnesses has enabled long-horizon LLM agents to function by relying on context engineering and file system access rather than custom cognitive architectures. Current applications, including coding and incident response, operate under a "first draft paradigm" where AI generates outputs for human review rather than aiming for fully autonomous deployment. To address non-deterministic behavior, developers now utilize trace analysis, online testing, and recursive self-improvement loops that allow agents to refine their own instructions based on historical execution data.
- Sequoia Capital8 min
Ambient Agents and the New Agent Inbox ft. Harrison Chase
LangChain CEO Harrison Chase introduces ambient agents as background systems designed to execute complex, multi-step operations by monitoring event streams rather than relying on direct chat input. These agents operate within a strict human-in-the-loop framework that utilizes patterns like action approval, editing, and time-travel rollback to ensure accuracy while preventing full autonomy. To support this architecture, LangChain has enhanced its LangGraph infrastructure for state persistence and scalability while deploying an Agent Inbox UI that enables users to manage long-running workflows through direct oversight and feedback integration.
- Sequoia Capital50 min
LangChain’s Harrison Chase on Building the Orchestration Layer for AI Agents | Training Data
Harrison Chase, Sonya Huang, Pat Grady
Harrison Chase positions Langchain as a critical orchestration layer for the "middle ground" of agent autonomy, prioritizing production-grade reliability over the volatile hype of fully autonomous systems. The company addresses this shift by deploying LangGraph for complex, stateful workflows and LangSmith for observability, enabling organizations to build custom cognitive architectures that balance flexibility with necessary human-in-the-loop controls. As the industry transitions from static chains to dynamic agents, these tools facilitate the move from customer support automation to software development integration while redefining testing and user experience paradigms.
- Sequoia Capital7 min
What's next for AI agents ft. LangChain's Harrison Chase
Harrison Chase positions LangChain as the dominant generative orchestration platform, while defining agent systems as loop-based entities that utilize language models to plan, act, and observe external tools. To address current reliability gaps in complex reasoning, the discourse advocates for "flow engineering" strategies that offload planning logic to human-designed state machines and introduces a "rewind and edit" user experience to facilitate human-in-the-loop correction. Furthermore, the next generation of agent applications is expected to integrate distinct procedural and personalized memory architectures, enabling systems to retain specific workflows and user preferences for enhanced personalization and task completion.