Techsplainers by IBM

In this episode of Techsplainers , we dive deeper into network observability, comparing it to network performance monitoring and DevOps observability. Learn how observability goes beyond static thresholds and predefined metrics to provide end-to-end visibility, richer context, and predictive analytics for dynamic network environments. We also explore why network observability is critical for indus...

In this episode of Techsplainers , we explore network observability, a proactive approach to understanding and managing complex network environments. Unlike traditional monitoring, which focuses on predefined metrics, network observability provides real-time visibility into network health and performance across on-premises, hybrid, and multicloud infrastructures. We break down its five pillars—met...

In this episode of Techsplainers , we explore the key differences between observability and monitoring and why both are critical for managing complex IT environments. Monitoring focuses on tracking predefined metrics and alerting teams when something goes wrong, while observability goes further by providing context and insights into why issues occur and how to fix them. We discuss how observabilit...

In this episode of Techsplainers , we break down the three pillars of observability: metrics, logs, and traces. We'll explain how they provide the foundation for understanding complex cloud-native systems. Discover what each pillar does, why they matter, and how they complement each other to deliver actionable insights for DevOps teams. We also explore system events, distributed tracing, and emerg...

This episode of Techsplainers takes a deep dive into observability, a cornerstone of modern DevOps and cloud-native environments. We break down what observability really means—going beyond traditional monitoring to provide full-stack visibility into complex systems. You’ll learn about its three pillars: logs, traces, and metrics, and how they work together to deliver actionable insights. The discu...

This episode of Techsplainers explains data quality management (DQM)—a set of practices that ensure data is accurate, complete, consistent, timely, unique, and valid. Learn why high-quality data is critical for business intelligence, regulatory compliance, and AI performance, and explore key techniques like data profiling, cleansing, validation, and monitoring.

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.

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 warehouse...

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.

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.

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), for...

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, applica...

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 o...

Join us on today's episode of Techsplainers as we delve into the world of AI agents, explaining their functions, capabilities, and operational components. We explore how AI agents leverage advanced natural language processing and tool calling to surpass traditional AI limitations, autonomously performing tasks and learning from experiences. Our discussion covers three key stages of AI agent operat...

This episode of Techsplainers introduces the concept of agentic AI, explaining how it differs from traditional AI models. Agentic AI, consisting of AI agents, operates autonomously and adaptively, using LLMs to function in dynamic environments. The episode discusses the benefits of agentic AI, including autonomy, proactivity, specialization, adaptability, and intuitiveness. Despite its potential,...

The official trailer for the newest audio podcast: Techsplainers by IBM. Visit the website to explore more: www.ibm.com/think/podcasts/techsplainers