The Future of AI Agents with Andrew Ng | Interrupt 26

Name: The Future of AI Agents with Andrew Ng | Interrupt 26
Uploaded: 2026-06-17T17:07:43.028Z
Duration: 31 min 40 s
Channel: LangChain
Description: Andrew Ng and Harrison Chase discuss the future of AI agents, including the evolution of coding agents, enterprise AI adoption, data architecture, and the role of tools like LangSmith in the development process.

LangChain17 June 2026Watch on YouTube

Part of series

Ep. 9 · LangSmith Engine: Agents bouwen

LangChain laat zien hoe LangSmith Engine het evalueren en verbeteren van AI-agents automatiseert via trace-analyse.

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Description

Andrew Ng sat down with Harrison Chase for a wide-ranging fireside chat at Interrupt, the agent conference by LangChain. Watch the full session and get insights on: - How fast coding agents have evolved and how the way small teams ship is being reshaped - The "product management bottleneck" and why accelerating software development just moves the constraint somewhere else - How enterprises are getting incremental AI wins but missing the bigger transformation - The data architecture rework most companies haven't started yet ...and so much more The Future of AI Agents with Andrew NG | Interrupt 26 0:00 Welcome and intro 0:39 What surprised Andrew most in the past year 1:06 The rise of coding agents 2:01 The product management bottleneck 4:50 What background do these new generalist engineers come from 6:02 How to get into AI software engineering 7:50 Building blocks, LEGO bricks, and combinatorial development 8:34 Context Hub and keeping agents up to date 9:50 DeepLearning.AI, LangChain, and short courses 10:05 How education is changing with AI 11:46 LearnDream.ai and interactive learning 13:29 Enterprise AI adoption: what's working and what isn't 14:46 The loan underwriting example 16:44 Cost savings vs. driving growth 17:11 Good examples of AI driving business growth 18:27 Measuring ROI 19:07 Swinging for the fences vs. incremental gains 22:00 Forward deployed engineers: hype vs. reality 23:01 Vendor lock-in and preserving optionality 25:04 LangSmith as a vendor-neutral layer 25:27 Open source and open-weight models 26:38 Data strategy before building agents 28:17 Unstructured data and the coming rearchitecture 31:22 Closing thoughts Extra resources: • Everything we shipped at Interrupt: https://www.langchain.com/blog/interrupt-2026-overview • Meet LangSmith Engine: https://www.langchain.com/blog/introducing-langsmith-engine • About LangChain: https://www.langchain.com/

What you'll learn

Coding agents are evolving rapidly and reshaping how small teams ship and develop software
The 'product management bottleneck' shows that accelerating software development merely shifts constraints to the next phase
Companies are achieving incremental AI wins but missing the larger transformation opportunity in their enterprise strategy
Most companies have not yet started the data architecture rework required to properly support AI agents
LangSmith serves as a vendor-neutral layer that allows teams to maintain flexibility and avoid vendor lock-in

Frequently asked questions

What is the 'product management bottleneck' that Andrew Ng mentions?

This is the phenomenon where accelerating software development with AI tools merely shifts constraints to the next phase, such as product management and planning, rather than solving the overall problem.

How do coding agents contribute to how small teams operate?

Coding agents are evolving rapidly and fundamentally changing how small teams develop and ship software, enabling fewer developers to accomplish more.

Why is data architecture important for AI agents in enterprises?

Data architecture is essential because most companies have not yet started this rework, yet it is crucial for successful AI agent implementation.

What role does LangSmith play in preventing vendor lock-in?

LangSmith functions as a vendor-neutral layer that helps teams maintain flexibility and avoid becoming dependent on a single vendor.