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

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 how AI is evolving beyond static models into adaptive experts with integrated memories. 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 unpack Chapter 19 of Keith Bourne's 'Unlocking Data with Generative AI and RAG,' exploring how advanced Retrieval-Augmented Generation (RAG) leverages episodic, semantic, and procedural memory types to create continuously learning AI agents that drive business value.

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:

- What advanced RAG with complete memory integration means for AI strategy

- The role of LangMem and the CoALA Agent Framework in adaptive learning

- Comparing learning algorithms: prompt_memory, gradient, and metaprompt

- Real-world applications across finance, healthcare, education, and customer service

- Key risks and challenges in deploying continuously learning AI

- Practical leadership advice for scaling and monitoring adaptive AI systems

Key tools & technologies mentioned:

- LangMem memory management system

- CoALA Agent Framework

- Learning algorithms: prompt_memory, gradient, metaprompt

Timestamps:

0:00 – Introduction and episode overview

2:15 – The promise of advanced RAG with memory integration

5:30 – Why continuous learning matters now

8:00 – Core architecture: Episodic, Semantic, Procedural memories

11:00 – Learning algorithms head-to-head

14:00 – Under the hood: How memories and feedback loops work

16:30 – Real-world use cases and business impact

18:30 – Risks, challenges, and leadership considerations

20:00 – Closing thoughts and next steps

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 AI insights, guides, and tools

Thanks for tuning in to Memriq Inference Digest - Leadership Edition.

Unlock how procedural memory transforms Retrieval-Augmented Generation (RAG) systems from static responders into autonomous, self-improving AI agents. Join hosts Morgan and Casey with special guest Keith Bourne as they unpack the concepts behind LangMem and explore why this innovation is a game-changer for business leaders.

In this episode:

- Understand what procedural memory means in AI and why it matters now

- Explore how LangMem uses hierarchical scopes and feedback loops to enable continuous learning

- Discuss real-world applications in finance, healthcare, and customer service

- Compare procedural memory with traditional and memory-enhanced RAG approaches

- Learn about risks, governance, and success metrics critical for deployment

- Hear practical leadership tips for adopting procedural memory-enabled AI

Key tools & technologies mentioned:

- LangMem procedural memory system

- LangChain AI orchestration framework

- CoALA modular architecture

- OpenAI's GPT models

Timestamps:

0:00 - Introduction and episode overview

2:30 - What is procedural memory and why it’s a breakthrough

5:45 - The self-healing AI concept and LangMem’s hierarchical design

9:15 - Comparing procedural memory with traditional RAG systems

12:00 - How LangMem works under the hood: feedback loops and success metrics

15:30 - Real-world use cases and business impact

18:00 - Challenges, risks, and governance best practices

19:45 - Final thoughts and next steps for leaders

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 insights, tools, and resources

Unlock how RAG-based agentic memory is transforming AI from forgetful chatbots into intelligent assistants that remember and adapt. In this episode, we break down the core concepts from Chapter 17 of Keith Bourne’s “Unlocking Data with Generative AI and RAG,” exploring why memory-enabled AI is a game changer for customer experience and operational efficiency.

In this episode, you’ll learn:

- What agentic memory means in AI and why it matters for leadership strategy

- The difference between episodic and semantic memory and how they combine

- Key tools like CoALA, LangChain, and ChromaDB that enable memory-enabled AI

- Real-world applications driving business value across industries

- The trade-offs and governance challenges leaders must consider

- Actionable tips for adopting RAG-based memory systems today

Key tools and technologies: CoALA, LangChain, ChromaDB, GPT-4, vector embeddings

Timestamps:

00:00 – Introduction and overview

02:30 – The AI memory revolution: episodic and semantic memory explained

07:15 – Why now: Technology advances driving adoption

10:00 – Comparing memory approaches: stateless vs episodic vs combined

13:30 – Under the hood: architecture and workflow orchestration

16:00 – Real-world impact and business benefits

18:00 – Risks, challenges, and governance

19:30 – Practical leadership takeaways and closing

Resources:

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

- Memriq.ai – Tools and resources for AI practitioners and leaders

Thanks for listening to Memriq Inference Digest - Leadership Edition.

Discover how agentic memory is transforming AI from forgetful assistants into adaptive, stateful partners that remember, learn, and evolve over time. In this episode, we unpack Chapter 16 of Keith Bourne’s 'Unlocking Data with Generative AI and RAG' and explore the strategic impact of extending Retrieval-Augmented Generation (RAG) with dynamic memory systems designed for real-world business advantage.

In this episode:

- What agentic memory is and why it matters for AI-driven products and services

- Comparison of leading agentic memory tools: Mem0, LangMem, Zep, and Graphiti

- How different memory types (working, episodic, semantic, procedural) enable smarter AI agents

- Real-world use cases across finance, healthcare, education, and tech support

- Technical architecture insights and key trade-offs for leadership decisions

- Challenges around memory maintenance, privacy, and compliance

Key tools & technologies mentioned:

- Mem0

- LangMem

- Zep

- Graphiti

- Vector databases

- Knowledge graphs

Timestamps:

0:00 - Introduction to Agentic Memory & RAG

3:30 - The strategic shift: from forgetful bots to adaptive AI partners

6:00 - Why now? Advances enabling stateful AI

8:30 - The CoALA framework: modeling AI memory like human cognition

11:00 - Tool head-to-head: Mem0, LangMem, Zep/Graphiti

14:00 - Under the hood: memory extraction and storage techniques

16:00 - Business impact: accuracy, latency, ROI

17:30 - Reality check: challenges and risks

19:00 - Real-world applications & leadership takeaways

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

Semantic caches are revolutionizing AI-powered applications by drastically reducing query latency and inference costs while improving response consistency. In this episode, we unpack Chapter 15 of Keith Bourne’s 'Unlocking Data with Generative AI and RAG' to explore how semantic caching works, why it’s critical now, and what it means for business leaders scaling AI.

In this episode:

- What semantic caches are and how they optimize AI workflows

- The business impact: slashing response times and inference costs by up to 100x

- Key technical components: vector embeddings, entity masking, and cross-encoder verification

- Real-world use cases across customer support, finance, and e-commerce

- Risks and best practices for tuning semantic caches to avoid false positives

- A practical decision framework for leaders balancing speed, accuracy, and cost

Key tools and technologies mentioned:

- Vector databases (ChromaDB)

- Sentence-transformer models

- Cross-encoder verification models

- Adaptive thresholding and cache auto-population

Timestamps:

0:00 – Introduction and overview of semantic caches

3:30 – Why semantic caches matter now: cost and latency challenges

6:45 – How semantic caches work: embeddings and entity masking

10:15 – Cross-encoder verification and precision vs. speed trade-offs

13:00 – Business payoff: latency reduction and cost savings

16:00 – Risks, pitfalls, and tuning best practices

18:30 – Real-world applications and industry examples

20:30 – Closing thoughts and next steps

Resources:

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

- Memriq AI – Visit https://Memriq.ai for AI tools, content, and resources

Unlock the power of Graph-Based Retrieval-Augmented Generation (RAG) with insights from Chapter 14 of Keith Bourne's 'Unlocking Data with Generative AI and RAG.' This episode explores how combining knowledge graphs with generative AI transforms accuracy, explainability, and multi-step reasoning—critical for leaders in regulated industries.

In this episode:

- Understand the core concept of Graph-Based RAG and why it’s a strategic game-changer now

- Compare traditional vector-based RAG with graph-driven approaches and their business implications

- Explore key tools like Protégé, Neo4j, LangChain, and OpenAI GPT-4o-mini powering this technology

- Learn how Python static dictionaries boost AI reasoning accuracy by up to 78%

- Discuss real-world applications in finance, healthcare, and enterprise knowledge management

- Review challenges like ontology governance, scalability, and ongoing innovation needs

Key tools and technologies mentioned:

- Protégé (ontology design)

- Neo4j (graph database)

- LangChain (AI workflow orchestration)

- OpenAI GPT-4o-mini (language model)

- Sentence-Transformers & FAISS (embedding and vector search)

Timestamps:

00:00 - Introduction to Graph-Based RAG and guest Keith Bourne

03:15 - Why Graph-Based RAG matters now for multi-hop reasoning and compliance

06:50 - The big picture: knowledge graphs, hybrid embeddings, and Python dictionaries

11:30 - Comparing approaches: traditional RAG vs. Microsoft GraphRAG vs. ontology-driven RAG

14:20 - Under the hood: tools, workflows, and code labs

17:00 - Practical payoffs, challenges, and real-world use cases

19:30 - Closing thoughts and next steps

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