Agent engineering is rapidly emerging as a pivotal discipline in AI development, promising autonomous LLM-powered systems that can perceive, reason, and act in complex, real-world environments. But is this truly a new engineering frontier or just a rebranding of existing ideas? In this episode, we dissect the technology, tooling, real-world deployments, and the hard truths behind the hype.

In this episode:

- Explore the origins and "why now" of agent engineering, including key advances like OpenAI's function calling and expanded context windows

- Break down core architectural patterns combining retrieval, tool use, and memory for reliable agent behavior

- Compare leading frameworks and SDKs like LangChain, LangGraph, AutoGen, Anthropic Claude, and OpenAI Agents

- Dive into production case studies from Klarna, Decagon, and TELUS showing impact and ROI

- Discuss the critical challenges around reliability, security, evaluation, and cost optimization

- Debate agent engineering vs. traditional ML pipelines and best practices for building scalable, observable agents

Key tools & technologies mentioned: LangChain, LangGraph, AutoGen, Anthropic Claude SDK, OpenAI Agents SDK, Pinecone, Weaviate, Chroma, FAISS, LangSmith, Arize Phoenix, DeepEval, Giskard

Timestamps:

00:00 - Introduction & episode overview

02:20 - The hype vs. reality: failure rates and market investments

05:15 - Why agent engineering matters now: tech enablers & economics

08:30 - Architecture essentials: retrieval, tool use, memory

11:45 - Tooling head-to-head: LangChain, LangGraph, AutoGen & SDKs

15:00 - Under the hood: example agent workflow and orchestration

17:45 - Real-world impact & production case studies

20:30 - Challenges & skepticism: reliability, security, cost

23:00 - Agent engineering vs. traditional ML pipelines debate

26:00 - Toolbox recommendations & engineering best practices

28:30 - Closing thoughts & final takeaways

Resources:

- "Unlocking Data with Generative AI and RAG" second edition by Keith Bourne - Search for 'Keith Bourne' on Amazon

- Memriq AI: https://memriq.ai

Thanks for tuning into Memriq Inference Digest - Engineering Edition. Stay curious and keep building!