Agent engineering is rapidly emerging as a transformative AI discipline, promising autonomous systems that do more than just talk—they act. But with high failure rates and market hype, how should leaders navigate this new terrain? In this episode, we unpack what agent engineering really means, its business impact, and how to separate strategic opportunity from hype.

In this episode, we explore:

- Why agent engineering is booming despite current 70% failure rates

- What agent engineering entails and how it differs from traditional AI roles

- Key tools and frameworks enabling reliable AI agents

- Real-world business outcomes and risks to watch for

- How to align hiring and investment decisions with your company’s AI strategy

Key tools & technologies mentioned:

- LangChain

- LangGraph

- LangSmith

- DeepEval

- AutoGen

Timestamps:

0:00 Intro & Topic Overview

2:30 The Agent Engineering Market Paradox

5:00 What is Agent Engineering?

7:30 Why Agent Engineering is Exploding Now

10:00 Agent Engineering vs. ML & Software Engineering

13:00 How Agent Engineering Works Under the Hood

16:00 Business Impact & Case Studies

18:30 Risks and Reality Checks

20:00 Final Takeaways & Closing

Resources:

- Unlocking Data with Generative AI and RAG by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Visit Memriq.ai for more AI leadership insights and resources