This episode of Techsplainers explains what a data fabric is and why it’s critical for modern enterprises. A data fabric is a data architecture that unifies and democratizes data access across hybrid and multicloud environments. It uses AI, metadata, and automation to break down silos, improve governance, and enable self-service data access—accelerating analytics, decision-making, and AI adoption. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Matt Finio

This episode of Techsplainers explores data fabric and how it unifies data, automates governance, and enables secure, self-service access for analytics and AI.

What is a data fabric?

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.

Techsplainers by IBM

This episode of Techsplainers explains what a data lakehouse is and why it’s transforming modern data management. A data lakehouse combines the low-cost, flexible storage of data lakes with the high-performance analytics of data warehouses, enabling unified data systems for advanced analytics and AI. Learn how lakehouses evolved, their architecture, and how they compare to data lakes and warehouses. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Matt Finio

This episode of Techsplainers explores data lakehouses and how they merge data lakes and warehouses to deliver scalable storage, fast analytics, and AI-ready architecture.

What is a data lakehouse?

This episode of Techsplainers explains what a data lake is, how it evolved, and why it’s a cornerstone of modern data architecture. We cover its role in storing massive amounts of raw data in any format, its cloud-based foundations, and how it supports AI and machine learning workloads. Plus, learn how data lakes compare to warehouses and lakehouses. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Matt Finio

This episode of Techsplainers dives into data lakes—what they are, how they work, and why they matter for big data, AI, and modern data architectures.

What is a data lake?

This episode of Techsplainers explains what data architecture is, why it matters, and how it shapes modern data strategies. From centralized and decentralized models to frameworks like TOGAF, we explore how a well-designed architecture enables scalability, governance, and advanced use cases like AI and real-time analytics. Done right, data architecture turns raw data into a strategic asset. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Matt Finio

This episode of Techsplainers breaks down data architecture and its role in data flow, governance, and AI readiness. Learn key concepts, models, and frameworks.

What is a data architecture?

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

Explore Agentic Architecture in our latest Techsplainers by IBM podcast. Compare Agentic vs non-Agentic systems, delve into different architectures &amp; frameworks.

What is agentic architecture?

What is agentic artchitecture?

In this episode of Techsplainers, host Alice explains the five main types of AI agents: simple reflex agents (like thermostats), model-based reflex agents (like robot vacuums), goal-based agents (like navigation robots), utility-based agents (like self-driving cars), and learning agents (like reinforcement learning systems). Each type is discussed in detail, highlighting its capabilities, applications, and limitations. The episode concludes by discussing the benefits of deploying multiple types of agents within a single system, emphasizing their potential in diverse industries for automation, optimization, and improved customer experiences. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Alice Gomstyn

Dive into the fascinating world of AI agents. Discover five key types: simple reflex, model-based, goal-based, utility-based, and learning agents. Learn how they're transforming industries with automation, optimization, and enhanced customer experiences.

Types of AI agents

This episode of Techsplainers provides an in-depth exploration of AI agents and AI assistants, comparing and contrasting their functionalities and capabilities. The podcast explains that AI assistants are reactive, performing tasks based on user commands, while AI agents are proactive, autonomously achieving specific goals. The discussion also delves into the structure and features of both types of AI, including conversational AI, prompts, recommendations, and tuning for assistants, and autonomy, decision-making, connectivity, persistent memory, and adaptive learning for agents. Real-world applications and potential benefits of both technologies are highlighted, along with current limitations and risks. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Alice Gomstyn

Discover the distinction between AI agents and assistants on this episode of Techsplainers by IBM. Learn about their unique features, applications, and the future implications in industries like banking, healthcare, and customer service.

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SEASON 1