Afleveringen
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Explore Think 2026: https://www.ibm.biz/think2026event
This episode of Techsplainers explores agentic coding in depth, building on our previous introduction. Matt explains how coding agents work across development stacks, distinguishing between vibe coding, agentic coding, and agentic engineering on the AI assistance spectrum. The episode highlights practical applications, from code reviews to feature development, while acknowledging the substantial benefits of handling mechanical coding tasks. Matt also addresses important challenges like subtle bugs and over-reliance, concluding with five best practices: defining guardrails, reviewing all AI-generated code, maintaining observability, providing proper context, and offering feedback to these AI collaborators. As with any powerful tool, thoughtful implementation is key to maximizing the value of agentic coding.
Find more information at https://www.ibm.com/think/topics/agentic-coding Find more episodes here https://www.ibm.biz/techsplainers-podcast Narrated by Matt Finio -
Explore Think 2026: https://www.ibm.biz/think2026event
This episode of Techsplainers explores the concept of AI sovereignty, the ability of organizations and nations to control their artificial intelligence ecosystem. We examine why AI sovereignty has evolved beyond traditional data residency concerns into a holistic strategy covering infrastructure, data, models, and operations. The discussion highlights the four core components of AI sovereignty: data sovereignty, operational sovereignty, digital sovereignty, and AI infrastructure. We also distinguish between AI sovereignty and sovereign AI, explore implementation approaches using public cloud, hybrid cloud, and on-premises solutions, and outline key benefits including enhanced security, regulatory compliance, operational resilience, and competitive advantage. Finally, we provide best practices for organizations looking to implement an effective AI sovereignty strategy in an increasingly AI-driven business landscape.
Find more information at https://www.ibm.com/think/topics/ai-sovereignty Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Matt Finio -
Zijn er afleveringen die ontbreken?
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Explore Think 2026: https://www.ibm.biz/think2026event This episode of Techsplainers delves into the intricate workings of quantum-centric supercomputing hardware and architecture. Host Ian Smalley explains the quantum processing unit (QPU) at the heart of these systems, describing how superconducting qubits function through Josephson junctions and require temperatures colder than space to maintain their quantum states. The episode outlines the three-phase evolution of quantum-centric supercomputing: from specialized compute engines within existing systems, to tightly coupled resources through advanced middleware, to fully co-designed quantum-HPC systems. Listeners will learn about key challenges facing this technology, including error correction approaches, scaling quantum processors through next-generation interconnects, and algorithm discovery. The discussion also covers IBM's ambitious roadmap toward systems with 2,000 logical qubits by 2033, providing insight into how this revolutionary computing paradigm will mature in the coming years. Find more information at www.ibm.com/think/topics/quantum-centric-supercomputing Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Ian Smalley -
Explore Think 2026: https://www.ibm.biz/think2026event This episode of Techsplainers introduces quantum-centric supercomputing, a revolutionary approach that combines quantum computing with traditional high-performance computing to create powerful integrated systems. Host Ian Smalley explains how these systems leverage the unique properties of qubits—including superposition, entanglement, interference, and decoherence—to potentially solve complex problems exponentially faster than classical computers alone. The episode covers the fundamental differences between classical and quantum computing, explores potential applications in pharmaceuticals, chemistry, and machine learning, and clarifies that quantum computing will complement rather than replace classical computing. Listeners will gain insight into this cutting-edge technology that IBM predicts will enable major breakthroughs in simulation, optimization, and solving challenging mathematical equations across multiple industries. Find more information at www.ibm.com/think/topics/quantum-centric-supercomputing Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Ian Smalley -
Explore Think 2026: https://www.ibm.biz/think2026event
This episode of Techsplainers introduces OpenRAG, IBM's open-source framework that connects large language models to enterprise data sources. We explore how OpenRAG builds bridges between powerful AI and organizational knowledge through Retrieval-Augmented Generation (RAG), enabling AI systems to ground their responses in actual company information rather than relying solely on training data. The discussion covers OpenRAG's flexible deployment options—from fully self-hosted architectures to hybrid cloud implementations—and highlights its modular design that allows organizations to customize components based on their specific needs. We examine real-world applications including enterprise knowledge assistants, customer support automation, regulatory compliance tools, research document analysis, data exploration interfaces, and collaborative knowledge systems. The episode concludes with practical guidance on getting started with OpenRAG, emphasizing its accessibility for both experimentation and enterprise-scale deployment.
Find more information at https://www.ibm.com/think/topics/openrag
Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Matt Finio -
Explore Think 2026: https://www.ibm.biz/think2026event
This episode of Techsplainers explores data retrieval, the essential process of accessing information from various data sources. We examine how this field has evolved beyond simple database queries to encompass complex AI-driven techniques. The discussion covers traditional approaches like SQL and indexing alongside modern methods including vector search, natural language processing, and retrieval augmented generation (RAG). We highlight how agentic RAG elevates retrieval capabilities through intelligent decision-making components like semantic caching, routing agents, and query planning. Real-world examples demonstrate impressive efficiency gains across healthcare, financial services, and e-commerce, while we also address challenges including data quality, security concerns, and vendor lock-in. As organizations manage ever-expanding data volumes and AI workloads, sophisticated data retrieval becomes increasingly critical to business success.
Find more information at https://www.ibm.com/think/topics/data-retrieval
Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Matt Finio -
Explore Think 2026: https://www.ibm.biz/think2026event This episode of *Techsplainers* explores multi-agent collaboration, where multiple AI agents work together as a coordinated team to accomplish complex tasks. We explain how these systems have evolved beyond traditional LLMs to create autonomous workflows for research, support, analysis, and operations. The discussion covers key collaboration models including rule-based, role-based, and model-based approaches, and examines leading frameworks like IBM's Bee Agent, LangChain, and OpenAI's Swarm. We also highlight Watsonx Orchestrate as an enterprise solution for orchestrating AI-enabled workflows through interconnected components. Throughout the episode, we use the analogy of drone teams searching disaster sites to illustrate how independent agents can coordinate effectively without centralized control to tackle complex challenges that would overwhelm a single agent.
Find more information at https://www.ibm.com/think/topics/multi-agent-collaboration Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Matt Finio -
This episode of Techsplainers explores data governance, the essential framework that ensures organizational data is properly managed, protected, and utilized. Amanda explains how data governance serves as an "air traffic control system" for information, defining policies and procedures for data collection, storage, and usage throughout its lifecycle. The discussion covers the four key components of governance frameworks: program goals and roles, data standards and policies, auditing procedures, and supporting tools. We examine how effective governance delivers tangible benefits including enhanced data value, balanced access, compliance with regulations like GDPR and HIPAA, and responsible AI development. The episode also addresses common implementation challenges such as lack of sponsorship, inconsistent architecture, and evolving AI requirements, before concluding with best practices including automation, creating a comprehensive data catalog, and continuous improvement. As the final installment in our data for AI series, this episode demonstrates how governance provides the structure that enables everything from AI-ready data to synthetic data creation.
Find more information at https://www.ibm.com/think/topics/unstructured-data
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Narrated by Amanda Downie
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This episode of Techsplainers explores synthetic data - artificially generated information designed to mimic real-world data while preserving statistical properties and patterns. Amanda explains how synthetic data has become critical for AI development by addressing issues of data scarcity, privacy concerns, and training needs. The discussion covers the three types of synthetic data (fully synthetic, partially synthetic, and hybrid) and various generation techniques including statistical methods, GANs, transformer models, VAEs, and agent-based modeling. We examine the significant benefits of synthetic data - customization flexibility, improved efficiency, enhanced privacy protection, and data enrichment - while also addressing challenges like bias propagation, model collapse, accuracy-privacy tradeoffs, and verification needs. The episode concludes with real-world applications across automotive, finance, healthcare, and manufacturing industries, demonstrating how synthetic data is becoming essential for AI development.
Find more information at https://www.ibm.com/think/topics/unstructured-data
Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Amanda Downie
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This episode of Techsplainers explores unstructured data - information without predefined formats that makes up 90% of enterprise data. Amanda explains how unstructured data differs from structured and semi-structured data, covering its diverse sources from emails to social media posts to sensor data. The discussion highlights why unstructured data has transformed from "dark data" into a strategic asset, particularly for AI applications. We explore key use cases including generative AI training, retrieval augmented generation (RAG), sentiment analysis, and predictive analytics. The episode also covers storage solutions like object storage and data lakes, plus processing tools that help organizations extract value from their unstructured information. With proper governance and management, unstructured data has become the fuel powering today's AI revolution.
Find more information at https://www.ibm.com/think/topics/unstructured-data
Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Amanda Downie
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This episode of Techsplainers explores the concept of "bad data" - information that compromises decision-making because it's inaccurate, incomplete, inconsistent, outdated, duplicate, invalid, or biased. We examine why bad data is particularly dangerous due to its stealthy nature, often going undetected until significant damage occurs. Through real-world examples like Unity Technologies' $110 million loss from bad data in their AI algorithms, we illustrate the severe consequences across industries from healthcare to finance. The discussion covers the diverse causes of data quality problems - from system failures and data decay to human error and integration challenges - and provides a comprehensive approach to prevention through governance, monitoring, cleansing, and data literacy. As organizations increasingly rely on AI systems, understanding that "garbage in, garbage out" applies more than ever becomes crucial for success in data-driven initiatives.
Find more information at https://www.ibm.com/think/topics/bad-data
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Narrated by Amanda Downie
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This episode of Techsplainers explores the concept of AI-ready data - high-quality, accessible, and trusted information that organizations need for successful artificial intelligence initiatives. We examine why only 29% of technology leaders believe their data meets AI readiness standards and break down the four essential characteristics that make data truly AI-ready: being unified and accessible, properly governed, secure, and supported by the right skills and infrastructure. The discussion highlights common barriers to AI readiness including data fragmentation, quality issues, skills gaps, and security risks, while explaining how organizations are failing to utilize their valuable unstructured data - with less than 1% of enterprise data currently leveraged in traditional large language models. Through practical examples and industry insights, this episode provides a roadmap for transforming raw data into a strategic asset that can power trusted, reliable AI applications across the enterprise.
Find more information at https://www.ibm.com/think/topics/ai-ready-data
Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Amanda Downie
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This episode of Techsplainers explores cloud architecture—the foundational blueprint for cloud computing environments. We break down the four essential components: the front-end (user interfaces and dashboards), the back-end (servers, databases, and infrastructure), the network (connections between components), and cloud delivery models (IaaS, PaaS, and SaaS). The discussion covers various deployment approaches, from public and private clouds to hybrid and multicloud environments, and explains how organizations strategically combine these models to optimize performance and security. We also highlight the role of cloud architects in orchestrating these complex environments and implementing best practices for automation, data management, and workload placement. Finally, we examine the business benefits of well-designed cloud architecture, including accelerated modernization, faster innovation, enhanced resilience, and improved security and compliance across environments.
Find more information at https://www.ibm.com/think/topics/cloud-architecture
Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Douglas Lambert
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This episode of Techsplainers explores private cloud computing—how it provides cloud benefits with enhanced security and control for organizations with sensitive data or specific regulatory requirements.
Find more information at https://www.ibm.com/think/topics/private-cloud
Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Douglas Lambert
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"This episode of Techsplainers explores public cloud computing, where third-party providers deliver computing resources over the internet on a pay-as-you-go basis. Douglas explains how public cloud works as a multi-tenant environment where users share virtualized resources while maintaining data isolation. The discussion covers the three primary service models—Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). The episode also compares public cloud with private and hybrid approaches, highlighting how organizations typically combine these models for optimal flexibility. Finally, we address security considerations in public cloud environments, noting how provider security has evolved to often surpass on-premises solutions despite requiring different management approaches.
Find more information at https://www.ibm.com/think/topics/public-cloud
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Narrated by Douglas Lambert
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This episode of Techsplainers explores the inner workings of cloud computing by examining its core components and service delivery models. Host Douglas Lambert explains the three fundamental building blocks of cloud computing: data centers (the physical infrastructure), networking capabilities (enabling high-speed connections), and virtualization (the technology that maximizes hardware efficiency). The episode then details the spectrum of cloud service models, from Infrastructure as a Service (IaaS), which provides basic computing resources, to Platform as a Service (PaaS), which offers development environments, to Software as a Service (SaaS), which delivers ready-to-use applications. The discussion concludes with an exploration of serverless computing, where providers automatically handle all infrastructure management and scale resources instantly based on demand. Through relatable analogies comparing these models to housing options—from unfurnished apartments to fully-serviced hotels—the episode demystifies the technical aspects of how cloud services are structured and delivered.
Find more information at https://www.ibm.com/think/topics/cloud-computing
Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Douglas Lambert
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This episode of Techsplainers introduces the fundamentals of cloud computing, explaining it as on-demand access to computing resources over the internet with pay-per-use pricing. Host Douglas Lambert breaks down how cloud computing powers everything from consumer applications like email and streaming services to critical business operations across organizations of all sizes. The discussion covers the evolution of cloud computing from its conceptual origins in the 1960s to its emergence as a business necessity in the early 2000s with pioneers like Amazon Web Services, Google, and Microsoft. Listeners will discover four key benefits that make cloud computing revolutionary: cost-effectiveness through pay-as-you-go models, enhanced speed and agility in deployment, unlimited scalability to match demand, and access to cutting-edge technologies without implementation hurdles. Through relatable analogies comparing cloud services to streaming platforms, this episode demystifies the concept of "the cloud" for both technical and non-technical audiences.
Find more information at https://www.ibm.com/think/topics/cloud-computing
Find more episodes at https://www.ibm.biz/techsplainers-podcast
Narrated by Douglas Lambert
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This episode of Techsplainers explores AI agent security - the critical frameworks, tools, and practices needed to ensure autonomous AI systems operate safely and responsibly. We examine the unique security challenges of AI agents compared to traditional cybersecurity, focusing on three key risk categories: threats targeting the agents themselves (like prompt injection and training data poisoning), risks in agent interactions with external systems (such as unauthorized data access and privilege escalation), and dangers from emergent agent behaviors that may have unintended consequences. The discussion covers essential security practices including least privilege access, authentication mechanisms, continuous monitoring, and circuit breakers to halt problematic actions. We also highlight the importance of sandboxing agents in controlled environments and conducting red team exercises to proactively identify vulnerabilities. As AI agents become more powerful and autonomous, implementing robust security measures becomes increasingly critical for responsible deployment across organizations. Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Cole Stryker
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This episode of Techsplainers explores AI agent evaluation - the systematic approaches used to assess the performance, capabilities, and limitations of autonomous AI systems. Unlike simpler AI models, agents require multidimensional evaluation frameworks that examine task performance, reasoning quality, safety, adaptability, efficiency, and user experience. We discuss various evaluation methodologies including benchmark testing, simulation-based evaluation, and human assessment, along with specific metrics organizations use to measure agent effectiveness. The episode also addresses the unique challenges of evaluating multi-agent systems, open-ended tasks, and ethical dimensions of agent behavior. Listeners will learn about emerging trends in agent evaluation, including automated assessment tools and sophisticated observability mechanisms that provide insight into agent decision-making processes. As AI agents become more capable and widely deployed, robust evaluation practices become increasingly essential for ensuring these systems perform reliably, safely, and effectively across diverse contexts. Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Cole Stryker
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This episode of Techsplainers explores AI agent governance - the essential frameworks and practices that ensure autonomous AI systems operate safely, ethically, and effectively. We examine how governance needs to span the entire agent lifecycle, from initial design decisions to ongoing operational oversight. The discussion covers key governance dimensions including access controls, tool usage permissions, decision authority, monitoring systems, feedback mechanisms, and accountability structures. We highlight core principles like transparency, human oversight, and continuous evaluation that underpin effective governance approaches. The episode also addresses emerging trends like adaptive controls and risk-based governance that calibrate oversight based on an agent's capabilities and potential impacts. As AI agents become more powerful and widespread, implementing robust governance becomes increasingly critical for organizations seeking to harness these technologies while managing their unique risks and ensuring alignment with human values and organizational goals. Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Cole Stryker
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