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 observability evolved from traditional application performance monitoring, the role of telemetry data (including logs, metrics, and traces), and how these tools work together to optimize performance. You will also learn about the benefits of observability for dynamic, cloud-native architectures and how AI-driven features enable predictive analytics and proactive issue resolution. This episode will help you understand how monitoring and observability create a powerful framework for reliability and scalability.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by PJ Hagerty

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 emerging capabilities like continuous profiling, which offer deeper visibility into application performance. Whether you are a developer, IT professional, or tech enthusiast, this episode will help you understand how observability accelerates troubleshooting, optimizes performance, and supports modern digital transformation.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by PJ Hagerty

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 discussion explores how observability empowers teams to troubleshoot faster, optimize performance, and improve user experience. We also examine cutting-edge innovations like AI-driven observability, predictive analytics, and causal AI, which are transforming how organizations prevent issues before they occur. Real-world benefits, common use cases, and the role of observability in accelerating DevOps pipelines round out this comprehensive guide to one of today’s most critical tech practices.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by PJ Hagerty

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.

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Narrated by Matt Finio

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.

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Narrated by Matt Finio

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 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 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 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.

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Narrated by Alice Gomstyn

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