In this bonus episode of Talking AI, Omar Shanti, CTO of HatchWorks AI, breaks down how LangChain’s abstractions have both helped and hindered AI innovation.

He explains why these tools, while useful for quick starts, can sometimes make simple tasks harder as projects scale. The conversation highlights where LangChain shines and where it falls short.

Viewers will hear insights on why prompt engineering and tuning are more challenging than building agent tools. Omar shares how over-abstraction can cause issues when taking projects into production, leading many to rethink their toolkits.

If you’re working with LangChain or similar abstraction tools, this episode gives you practical advice on avoiding common pitfalls and understanding when these abstractions might not serve your needs.

Key Moments:

Introduction to LangChain’s abstractions
What is abstraction?
LangChain’s pros and cons
Challenges with LangChain in production
Observability and orchestration
How much orchestration do you need?
Final thoughts on using LangChain

Key links:

Mentioned in this episode:

AI Opportunity Finder

Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

#228 - Live vanaf Bitcoin Amsterdam - Woensdag