Techsplainers by IBM

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/techsplainersNarrated by PJ Hagerty

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decision-making, compliance, AI, and customer satisfaction, and the common causes of inaccuracies—from human error to outdated information and biased data.Find more information at https://www.ibm.com/think/podcasts/techsplainersNarrated by Matt Finio

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/techsplainersNarrated by Matt Finio

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/techsplainersNarrated by Alice Gomstyn

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/techsplainersNarrated by Alice Gomstyn