This episode of Techsplainers explains what data integrity is, why it matters, and how organizations can maintain it. We cover the processes and security measures that ensure data remains accurate, complete, and consistent throughout its lifecycle. Learn why data integrity is critical for analytics, compliance, and trust, and explore the five key types of data integrity. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Matt Finio

This episode of Techsplainers breaks down data integrity—what it is, why it’s essential for analytics and compliance, and how to maintain it across your organization.

What is data integrity?

In this episode of Techsplainers, we dive into SRE observability, a critical practice for ensuring site reliability in today’s dynamic, cloud-native environments. Discover how SRE observability goes beyond traditional monitoring by using telemetry data—metrics, logs, and traces—to provide deep visibility into complex systems. We explain how it supports proactive issue detection, faster incident response, and data-driven decision-making. You will also learn about real-world use cases in ecommerce, finance, logistics, and healthcare, as well as emerging trends like AI-driven observability and causal AI. Whether you are an engineer, IT professional, or tech enthusiast, this episode will help you understand how SRE observability optimizes performance, enhances user experience, and drives better business outcomes. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by PJ Hagerty

This episode of Techsplainers explores SRE observability and its role in maintaining secure, scalable, high-availability systems. Learn how it combines DevOps practices with advanced tools to deliver real-time insights, proactive issue detection, and improved business outcomes.

What is SRE observability?

This episode of Techsplainers explains what data accuracy is, why it matters, and how organizations can achieve it. We explore its role as a core dimension of data quality, the benefits of accurate data for decision-making, compliance, AI, and customer satisfaction, and the common causes of inaccuracies—from human error to outdated information and biased data. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Matt Finio

This episode of Techsplainers covers data accuracy—its importance, benefits, and causes of inaccuracies. Learn how accurate data drives trust, compliance, and AI success.

What is data accuracy?

This episode of "Techsplainers" explains the concept of multi-agent collaboration. It discusses how multi-agent systems, comprising multiple AI agents, coordinate actions in a distributed system to achieve complex tasks. These tasks, once handled only by large language models, now include customer service triage, financial analysis, technical troubleshooting, and more. The podcast details how agents communicate via established protocols to exchange information, assign responsibilities, and coordinate actions. It also highlights the benefits of multi-agent collaboration, such as scalability, fault tolerance, and emergent cooperative behavior, using examples like a fleet of drones searching a disaster site. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Alice Gomstyn

Join us on "Techsplainers by IBM" as we delve into multi-agent collaboration, exploring how multiple AI agents coordinate actions to tackle complex tasks such as customer service triage and financial analysis. Discover the benefits of decentralized control, specialized AI agents, and retrieval augmented generation frameworks.

What is multi-agent collaboration?

This episode of Techsplainers introduces listeners to the concept of agentic architecture, a framework used for structuring AI agents to automate complex tasks. The podcast explains that agentic architecture is crucial for creating AI agents capable of autonomous decision-making and adapting to dynamic environments. It delves into the four core factors of agency: intentionality (planning), forethought, self-reactiveness, and self-reflectiveness. These four factors underpin AI agents' autonomy. The discussion also contrasts agentic and non-agentic architectures, highlighting the advantages of agentic architectures in supporting agentic behavior in AI agents. The podcast further breaks down different types of agentic architectures – single-agent, multi-agent, and hybrid – detailing their structures, strengths, weaknesses, and best use cases. Finally, it covers three types of agentic frameworks—reactive, deliberative, and cognitive—concluding with a detailed explanation of BDI architectures, a model for rational decision-making in intelligent agents. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Alice Gomstyn

Dive into the world of multi-agent systems in our latest Techsplainers by IBM episode. Learn how multiple AI agents work together to solve complex tasks, and explore real-world applications in transportation, healthcare, supply chains, and defense.

What is a multi-agent system?

This episode of&nbsp;Techsplainers&nbsp;introduces vibe coding, the practice of using AI tools to generate software code through natural language prompts rather than manual coding. We explore how this approach follows a "code first, refine later" philosophy that prioritizes experimentation and rapid prototyping. The podcast walks through the four-step implementation process: choosing an AI coding assistant platform, defining requirements through clear prompts, refining the generated code, and reviewing before deployment. While highlighting vibe coding's ability to accelerate development and free human creativity, we also examine its limitations—including challenges with technical complexity, code quality, debugging, maintenance, and security concerns. The discussion concludes by examining how vibe coding is driving paradigm shifts in software development through quick prototyping, problem-first approaches, reduced risk with maximized impact, and multimodal interfaces that combine voice, visual, and text-based coding methods to create more intuitive development environments. Find more information at&nbsp;<a href="https://www.ibm.com/think/podcasts/techsplainers" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/podcasts/techsplainers</a> Narrated by Amanda Downie

This episode of Techsplainers explores vibe coding—using AI to generate software code from natural language prompts, accelerating development while freeing humans for creative work.

What is vibe coding?

This episode of&nbsp;Techsplainers&nbsp;explores retrieval augmented generation (RAG), a powerful technique that enhances generative AI by connecting models to external knowledge bases. We examine how RAG addresses critical limitations of large language models—their finite training data and knowledge cutoffs—by allowing them to access up-to-date, domain-specific information in real-time. The podcast breaks down RAG's five-stage process: from receiving a user query to retrieving relevant information, integrating it into an augmented prompt, and generating an informed response. We dissect RAG's four core components—knowledge base, retriever, integration layer, and generator—explaining how they work together to create a more robust AI system. Special attention is given to embedding and chunking processes that transform unstructured data into searchable vector representations. The episode highlights RAG's numerous benefits, including cost efficiency compared to fine-tuning, reduced hallucinations, enhanced user trust through citations, expanded model capabilities, improved developer control, and stronger data security. Finally, we showcase diverse real-world applications across industries, from specialized chatbots and research tools to personalized recommendation engines. Find more information at&nbsp;<a href="https://www.ibm.com/think/podcasts/techsplainers " rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/podcasts/techsplainers </a> Narrated by Amanda Downie

This episode of Techsplainers explores retrieval augmented generation (RAG)—how connecting AI models to external knowledge sources creates more accurate, up-to-date responses.

What is retrieval augmented generation (RAG)?

Introducing the Techsplainers by IBM podcast, your new podcast for quick, powerful takes on today’s most important AI and tech topics. Each episode brings you bite-sized learning designed to fit your day, whether you’re driving, exercising, or just curious for something new. This is just the beginning. Tune in every weekday at 6 AM ET for fresh insights, new voices, and smarter learning.

What is data integrity?

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Recent Episodes

What is data integrity?

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Recent Episodes

What is SRE observability?

What is data accuracy?

What is multi-agent collaboration?

What is a multi-agent system?

What is vibe coding?

What is retrieval augmented generation (RAG)?