Discover how the Model Context Protocol (MCP) is revolutionizing AI system integration by simplifying complex connections between AI models and external tools. This episode breaks down the technical and strategic impact of MCP, its rapid adoption by industry giants, and what it means for your AI strategy.

In this episode:

- Understand the M×N integration problem and how MCP reduces it to M+N, enabling seamless interoperability

- Explore the core components and architecture of MCP, including security features and protocol design

- Compare MCP with other AI integration methods like OpenAI Function Calling and LangChain

- Hear real-world results from companies like Block, Atlassian, and Twilio leveraging MCP to boost efficiency

- Discuss the current challenges and risks, including security vulnerabilities and operational overhead

- Get practical adoption advice and leadership insights to future-proof your AI investments

Key tools & technologies mentioned:

- Model Context Protocol (MCP)

- OpenAI Function Calling

- LangChain

- OAuth 2.1 with PKCE

- JSON-RPC 2.0

- MCP SDKs (TypeScript, Python, C#, Go, Java, Kotlin)

Timestamps:

0:00 - Introduction to MCP and why it matters

3:30 - The M×N integration problem solved by MCP

6:00 - Why MCP adoption is accelerating now

8:15 - MCP architecture and core building blocks

11:00 - Comparing MCP with alternative integration approaches

13:30 - How MCP works under the hood

16:00 - Business impact and real-world case studies

18:30 - Security challenges and operational risks

21:00 - Practical advice for MCP adoption

23:30 - Final thoughts and strategic takeaways

Resources:

• "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition
• This podcast is brought to you by Memriq.ai - AI consultancy and content studio building tools and resources for AI practitioners.

Retrieval-Augmented Generation (RAG) combined with reference-free evaluation is revolutionizing how AI engineers monitor and improve large language model deployments at scale. This episode unpacks the architecture, trade-offs, and real-world impact of using LLMs as judges rather than relying on costly ground truth datasets.

In this episode:

- Explore why traditional evaluation metrics fall short for RAG systems and how reference-free methods enable continuous, scalable monitoring

- Dive into the atomic claim verification pipeline and how LLMs assess faithfulness, relevancy, and context precision

- Compare key open-source and commercial tools: RAGAS, DeepEval, TruLens, and Weights & Biases

- Learn from real-world deployments at LinkedIn, Deutsche Telekom, and healthcare providers

- Discuss biases, limitations, and practical engineering patterns for production-ready evaluation pipelines

- Hear expert tips on integrating evaluation with CI/CD, observability, and hybrid human-in-the-loop workflows

Key tools and technologies mentioned:

- RAGAS (Reference-free Atomic Generation Assessment System)

- DeepEval

- TruLens

- Weights & Biases

- LangChain, LlamaIndex

- OpenAI GPT-4o-mini, Anthropic Claude, Google Gemini, Ollama

- Embedding models (text-embedding-ada-002)

Timestamps:

00:00 Intro and episode overview

02:15 The promise of LLMs as reliable self-evaluators

05:30 Why traditional metrics fail for RAG

08:00 Reference-free evaluation pipeline deep dive

11:45 Head-to-head comparison of evaluation tools

14:30 Under the hood: RAGAS architecture and scaling

17:00 Real-world impact and deployment stories

19:30 Pitfalls and biases to watch for

22:00 Engineering best practices and toolbox tips

25:00 Book spotlight and closing thoughts

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 at https://Memriq.ai for more AI engineering deep-dives, guides, and research breakdowns

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

Is your AI initiative falling short despite the hype? The root cause often lies not in the AI technology itself but in how your architecture handles the Natural Language Understanding (NLU) layer. In this episode, we explore why treating AI as a bolt-on feature leads to failure and what leadership must do to embrace the fundamental paradigm shift required for success.

In this episode, you'll learn:

- Why legacy deterministic web app architectures break when faced with conversational AI

- The critical role of the NLU layer as the "brain" driving dynamic, user-led interactions

- How multi-intent queries, partial understanding, and fallback strategies redefine system design

- The importance of AI-centric orchestration bridging probabilistic AI reasoning with deterministic backend execution

- Practical architectural patterns like the 99-intents fallback and context management to improve reliability

- How to turn unsupported user requests into upsell and engagement opportunities

Key tools and technologies mentioned include Large Language Models (LLMs), function-calling APIs, AI orchestration layers, and frameworks from thought leaders like Keith Bourne, Ivan Westerhof, and Sunil Ramlochan.

Timestamps:

0:00 - Introduction & Why AI Projects Fail

3:30 - The NLU Paradigm Shift Explained

7:15 - User Perspective vs. System Reality

10:20 - Handling Multi-Intent & Partial Understanding

13:10 - Architecting Fallbacks & Out-of-Scope Requests

16:00 - Business Impact & ROI of Robust NLU Architectures

18:30 - Closing Thoughts & Leadership Takeaways

Resources:

• "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition
• This podcast is brought to you by Memriq.ai - AI consultancy and content studio building tools and resources for AI practitioners.

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