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!

Discover how the Natural Language Understanding (NLU) layer transforms traditional web apps into intelligent AI chatbots that understand open-ended user input. This episode unpacks the architectural shifts, business implications, and governance challenges leaders face when adopting AI-driven conversational platforms.

In this episode:

- Understand the strategic role of the NLU layer as the new ‘brain’ interpreting user intent and orchestrating backend systems dynamically.

- Explore the shift from deterministic workflows to probabilistic AI chatbots and how hybrid architectures balance flexibility with control.

- Learn about key AI tools like Large Language Models, Microsoft Azure AI Foundry, OpenAI function-calling, and AI agent frameworks.

- Discuss governance strategies including confidence thresholds, policy wrappers, and human-in-the-loop controls to maintain trust and compliance.

- Hear real-world use cases across industries showcasing improved user engagement and ROI from AI chatbot adoption.

- Review practical leadership advice for monitoring, iterating, and future-proofing AI chatbot architectures.

Key tools and technologies mentioned:

- Large Language Models (LLMs)

- Microsoft Azure AI Foundry

- OpenAI Function-Calling

- AI Agent Frameworks like deepset

- Semantic Cache and Episodic Memory

- Governance tools: Confidence thresholds, human-in-the-loop

Timestamps:

00:00 - Introduction and episode overview

02:30 - Why the NLU layer matters for leadership

05:15 - The big architectural shift: deterministic to AI-driven

08:00 - Comparing traditional web apps vs AI chatbots

11:00 - Under the hood: how NLU, function-calling, and orchestration work

14:00 - Business impact and ROI of AI chatbots

16:30 - Risks, governance, and human oversight

18:30 - Real-world applications and industry examples

20:00 - Final takeaways and leadership advice

Resources:

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

- Visit Memriq at https://Memriq.ai for more AI insights and resources

Unlock the next level of Retrieval-Augmented Generation with full memory integration in AI agents. In the previous 3 episodes, we secretly built up what amounts to a 4-part series on agentic memory. This is the final piece of that 4-part series that pulls it ALL together.

In this episode, we explore how combining episodic, semantic, and procedural memories via the CoALA architecture and LangMem library transforms static retrieval systems into continuously learning, adaptive AI.

This also concludes our book series, highlighting ALL of the chapters of the 2nd edition of "Unlocking Data with Generative AI and RAG" by Keith Bourne. If you want to dive even deeper into these topics and even try out extensive code labs, search for 'Keith Bourne' on Amazon and grab the 2nd edition today!

In this episode:

- How CoALAAgent unifies multiple memory types for dynamic AI behavior

- Trade-offs between LangMem’s prompt_memory, gradient, and metaprompt algorithms

- Architectural patterns for modular and scalable AI agent development

- Real-world metrics demonstrating continuous procedural strategy learning

- Challenges around data quality, metric design, and domain agent engineering

- Practical advice for building safe, adaptive AI agents in production

Key tools & technologies: CoALAAgent, LangMem library, GPT models, hierarchical memory scopes

Timestamps:

0:00 Intro & guest welcome

3:30 Why integrating episodic, semantic & procedural memory matters

7:15 The CoALA architecture and hierarchical learning scopes

10:00 Comparing procedural learning algorithms in LangMem

13:30 Behind the scenes: memory integration pipeline

16:00 Real-world impact & procedural strategy success metrics

18:30 Challenges in deploying memory-integrated RAG systems

20:00 Practical engineering tips & closing thoughts

Resources:

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

- Memriq AI: https://memriq.ai