Techsplainers by IBM

This episode of Techsplainers explores Code LLMs, specialized AI models that are transforming software development by understanding, generating, and explaining code. We examine how these models differ from general-purpose LLMs through their extensive training on programming languages, documentation, and code repositories, giving them a deeper understanding of software development concepts and patterns. The discussion covers how Code LLMs work through transformer architecture and reinforcement learning from human feedback, as well as their substantial benefits for developers, including increased productivity, enhanced learning opportunities, improved documentation, and democratized access to coding. We highlight practical applications such as code generation, bug fixing, refactoring, and test creation, while also addressing important limitations including potential security vulnerabilities, challenges with complex code, intellectual property concerns, and the risk of over-reliance. The episode concludes with insights into how these tools are reshaping the development landscape, with developers increasingly shifting toward a supervisory role focused on architecture and design decisions. Find more information at https://www.ibm.biz/techsplainers-podcastNarrated by Erika Russi

This episode of Techsplainers explores Integration Platform as a Service (iPaaS), a cloud-native solution that connects disparate business systems and applications. We examine how iPaaS differs from traditional middleware approaches and note key use cases including data synchronization, streamlined automations, AI-powered optimization, governance, and B2B integration.   The episode highlights industry-specific applications in healthcare, banking, and manufacturing, along with significant benefits like operational efficiency, improved accessibility through no-code tools, and enhanced security. We also discuss future trends, including how iPaaS helps combat SaaS sprawl and leverage unstructured data for AI training and autonomous agent development. With organizations achieving up to 345% ROI after implementation, iPaaS is becoming an essential component of modern digital transformation strategies. Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Dan Segal 

This episode of Techsplainers explores Integration Platform as a Service (iPaaS), a cloud-based solution that connects applications, systems, and data sources across diverse IT environments. We explain how iPaaS emerged to address the challenge of SaaS sprawl—where organizations use hundreds of different applications—and how it offers pre-built connectors, low-code interfaces, and centralized monitoring. The episode walks through how iPaaS works, how it compares to traditional approaches like Enterprise Service Buses and API management, and its various use cases from app-to-app integration to AI-powered workflows. Listeners will learn about the benefits of iPaaS, including reduced complexity, lower costs, improved data accessibility, and increased scalability, all of which help organizations streamline operations and break down data silos in increasingly complex IT ecosystems. Find more information at https://www.ibm.biz/techsplainers-podcast Narrated by Dan Segal 

This episode of Techsplainers explores enterprise application integration (EAI), the crucial technology that connects disparate business systems and software applications across organizations. We explain how EAI works through both synchronous and asynchronous processing methods, and breaks down five key architectural patterns including point-to-point, hub and spoke, service-oriented architecture, microservices, and event-driven approaches.   The discussion covers how EAI compares to related technologies like iPaaS, EDI, and ERP systems, while highlighting major benefits including legacy system integration, elimination of data silos, and increased business agility. The episode also addresses challenges like security vulnerabilities, migration issues, and performance limitations, before concluding with a look at how modern innovations like AI-powered integration and low-code tools are transforming EAI for today's enterprise environments. Find more information at https://www.ibm.com/think/podcasts/techsplainers  Narrated by Dan Segal 

This episode of Techsplainers explores EDI integration, the critical process that connects electronic data interchange platforms with an organization's internal systems. We examine how EDI integration creates automated data highways that eliminate manual processes by transforming standardized digital documents like purchase orders and shipping notices between different systems.   The discussion covers the key benefits of EDI integration, including operational efficiency gains, cost reductions and improved data quality, along with various architectural patterns from centralized hub-and-spoke to hybrid API-EDI approaches. We also explore connectivity considerations, from direct point-to-point integration to value-added networks, and look at emerging trends like AI-enhanced integration, deeper ERP connectivity, and self-service options that are making EDI more accessible across industries.  Find more information at https://www.ibm.com/think/podcasts/techsplainers  Narrated by Dan Segal  

This episode of Techsplainers explores electronic data interchange (EDI), the standardized system for computer-to-computer exchange of business documents like invoices and purchase orders. We explain how EDI works through specialized translator software and transmission protocols, and details the major standards including ANSI ASC X12, HIPAA, and EDIFACT. The discussion covers EDI's substantial benefits: time and cost savings, error reduction, and improved business analysis capabilities. The episode also examines how EDI is evolving through AI integration for fraud detection and autonomous processing, while comparing EDI with APIs to show how these technologies complement each other for different business needs. Despite being decades old, EDI continues to process trillions of dollars in commerce annually across major industries worldwide.  Find more information at https://www.ibm.com/think/podcasts/techsplainers  Narrated by Dan Segal 

This episode of Techsplainers explores hierarchical AI agents, sophisticated systems where multiple AI components work together in a structured, tiered fashion to tackle complex problems. We examine the three levels of agents—high-level strategic planners, mid-level coordinators, and specialized low-level executors—and how they communicate to accomplish goals efficiently. The episode details key features like agent hierarchy, task decomposition, specialization, and feedback-driven coordination that make these systems effective. We also investigate real-world applications in supply chain management, manufacturing, cybersecurity, and autonomous vehicles, while discussing the benefits of modularity, efficiency, scalability, and fault tolerance alongside challenges like complexity, rigidity, and communication bottlenecks that organizations must navigate when implementing these powerful AI systems.  Find more information at https://www.ibm.com/think/podcasts/techsplainers  Narrated by Matt Finio 

This episode of Techsplainers explores utility-based agents, sophisticated AI systems that use mathematical utility functions to make optimal decisions by weighing multiple competing objectives. We examine the five key components of these agents: utility functions, sensors, internal models, action selection mechanisms, and actuators. The episode walks through their decision-making workflow and highlights applications in smart homes, self-driving cars, healthcare, and e-commerce. While utility-based agents offer advantages in adaptability, flexibility, and reliability over simpler AI systems, they also present challenges in computational requirements and the ethical considerations of translating human values into mathematical formulas. Understanding these advanced agents provides insight into how AI can make complex trade-offs in uncertain environments. Find more information at https://www.ibm.com/think/podcasts/techsplainers  Narrated by Matt Finio 

This episode of Techsplainers explores goal-based agents, which sit in the middle of the AI agent complexity hierarchy. These agents go beyond simple reflexes by incorporating planning capabilities that consider future states when making decisions. The podcast explains how goal-based agents work through four stages: goal definition, planning, action selection, and execution. We examine a real-world example of warehouse automation robots that plan efficient paths rather than simply reacting to obstacles. The episode also discusses when to use goal-based agents versus more complex types like utility-based agents, and how different agent types can work together in multi-agent systems, as illustrated through a healthcare example where five specialized agents handle different aspects of hospital management based on their complexity requirements. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Matt Finio 

This episode of Techsplainers explores model-based reflex agents, a type of AI that makes decisions using both current input and an internal model of its environment. Unlike simple reflex agents that only react to immediate stimuli, model-based agents maintain memory of past perceptions and can predict how their actions might affect their surroundings. We examine the four key components—sensors, internal model, reasoning component, and actuators—and the four-stage behavioral loop these agents follow: sensing, internal modeling, decision-making, and action. The discussion highlights use cases in autonomous vehicles, robotics, gaming, and enterprise automation, while comparing them with other agent types including goal-based, utility-based, learning, and hierarchical agents. Finally, we address the limitations of model-based reflex agents, from computational requirements to their inability to adapt their rulesets over time.  Find more information at https://www.ibm.com/think/podcasts/techsplainers  Narrated by Matt Finio 

This episode of Techsplainers explores simple reflex agents, the most basic type of AI agents that operate on straightforward "if-this-then-that" logic. We examine how these agents directly respond to their environment based on predefined rules, without considering past experiences or future consequences. The discussion covers real-world examples like thermostats, factory safety systems, and quality control monitors, highlighting the benefits of these agents: computational efficiency, instantaneous response times, predictable behavior, and cost-effectiveness. We also address their limitations, including lack of memory, inability to handle uncertainty, and inflexibility when facing new situations. Finally, we demonstrate how simple reflex agents can work effectively as part of multi-agent systems, providing critical safety backstops while more sophisticated agents handle complex decision-making.  Find more information at https://www.ibm.com/think/podcasts/techsplainers  Narrated by Matt Finio 

This episode of Techsplainers explores cloud storage—a service that allows data and files to be stored offsite by third-party providers and accessed via the internet or private networks. We examine how cloud storage works through virtual servers in massive data centers, with data replicated across multiple machines for redundancy. The discussion covers four different cloud storage environments (public, private, hybrid, and multicloud) and three main types of storage solutions (file, block, and object). We also highlight the significant benefits of cloud storage, including offsite management, fast implementation, cost-effectiveness, and virtually unlimited scalability. Security considerations, compliance tools, and pricing models are explained, along with common use cases ranging from team collaboration to AI and data analytics. With the cloud storage market projected to grow from $108.7 billion in 2023 to $665 billion by 2032, this technology continues to transform how organizations of all sizes manage their ever-increasing data volumes. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Daniela Baez

This episode of Techsplainers explores the concept of multicloud—the strategic use of cloud services from more than one provider. We examine how organizations leverage multicloud to optimize performance, control costs, and avoid vendor lock-in while maintaining flexibility to adopt the best technologies as they emerge. The discussion covers the differences between simple SaaS usage and more complex enterprise multicloud scenarios using PaaS and IaaS from major providers like AWS, Google Cloud, IBM Cloud, and Microsoft Azure. We also address the challenges of multicloud management and how organizations use centralized platforms with AI capabilities to maintain consistent security, compliance, and operational efficiency across diverse cloud environments. Finally, we clarify the relationship between multicloud and hybrid cloud, explaining how most enterprise environments today are actually hybrid multiclouds that combine the benefits of both approaches for maximum business value. Find more information at https://www.ibm.com/think/podcasts/techsplainers.Narrated by Daniela Baez

This episode of Techsplainers explores virtualization—the foundational technology that enables the creation of multiple virtual environments from a single physical machine. We trace its evolution from IBM's early experiments in 1964 to today's $85 billion industry powering cloud computing worldwide. The episode explains how virtualization works through hypervisors—software that creates and manages virtual machines—and dives into its many benefits, including resource efficiency, easier management, minimal downtime, and cost savings. We also explore the various types of virtualization beyond servers, including desktop, network, storage, and application virtualization, while comparing traditional VM-based virtualization with newer containerization approaches. Whether you're running a massive data center or simply want to run multiple operating systems on your laptop, this episode provides a comprehensive overview of this essential technology that makes modern computing more efficient, flexible, and resilient. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Daniela Baez

This episode of Techsplainers explores cloud infrastructure—the foundational hardware and software components that make cloud computing possible. We dive into the four key elements of cloud infrastructure: servers (both physical and virtual), storage solutions for various data types, networking components that enable communication between resources, and management software that ties everything together. The discussion covers virtualization technology and hypervisors that create multiple virtual machines from physical hardware, as well as modern cloud-native approaches using containers and microservices. We also examine different deployment models, including public, private, hybrid, and multicloud, along with service delivery options like IaaS, PaaS, SaaS, and serverless computing. Finally, we highlight the significant benefits cloud infrastructure offers: reliability through redundancy, agility for rapid deployment, elasticity to handle variable workloads, cost optimization through pay-as-you-go models, and robust disaster recovery capabilities. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Daniela Baez

This episode of Techsplainers introduces hybrid cloud—a flexible IT approach that combines public cloud, private cloud, and on-premises infrastructure into a unified environment. We explore the core components of hybrid cloud architecture including network connectivity, virtualization, containerization, and management platforms, while tracing its evolution from traditional physical connections to modern workload portability across environments. The discussion highlights how businesses leverage hybrid multicloud to improve developer productivity, optimize infrastructure spending, enhance security compliance, and accelerate innovation. You'll learn about real-world applications, including regulatory compliance, scalability, legacy app enhancement, and disaster recovery. We also examine how hybrid cloud is enabling next-generation technologies like generative AI, with insights into the explosive market growth projected to reach $558.6 billion by 2032. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Daniela Baez

This episode of Techsplainers explores two fundamental capabilities of AI agents: communication and learning. We examine how AI agents exchange information with each other and humans, including agent-to-agent protocols like KQML and FIPA-ACL. We also look at the challenges they face with standardization, ambiguity, latency, and security. The discussion then shifts to how agents learn and improve over time, covering supervised learning with labeled data, unsupervised learning that finds patterns without human oversight, and reinforcement learning through trial and error with rewards. We also explore continuous learning, where agents adapt to new information without forgetting previous knowledge, and how these capabilities combine in multi-agent systems to create collaborative intelligence that can solve complex problems across various industries. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Selma Pacheco Jimenez

This episode of Techsplainers explores the concept of tool calling in artificial intelligence, explaining how it enables AI models to interact with external tools, APIs, and systems beyond their native capabilities. We walk through how tool calling works, from recognizing when external assistance is needed to selecting appropriate tools and processing responses. The episode highlights the powerful combination of tool calling with retrieval augmented generation (RAG) and examines real-world applications in information retrieval, code execution, process automation, IoT device control, and personalized recommendations. By bridging the gap between AI reasoning and action, tool calling is transforming passive AI assistants into proactive digital agents capable of completing complex, multi-step tasks through dynamic access to external resources. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Selma Pacheco Jimenez

This episode of Techsplainers explores the crucial components of AI agent memory and agentic reasoning. We delve into how AI agents store and recall information through different memory types—including short-term, long-term, episodic, semantic, and procedural memory—and how frameworks like LangChain and LangGraph implement these capabilities. The episode also examines various reasoning paradigms that power AI decision-making, from simple conditional logic to sophisticated approaches like ReAct, ReWOO, and multiagent reasoning. By understanding these complementary components, listeners gain insight into how modern AI systems transform from passive models into intelligent agents that can maintain context across interactions, learn from past experiences, and make autonomous decisions to achieve complex goals. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Selma Pacheco Jimenez

This episode of Techsplainers explores two fundamental capabilities of AI agents: perception and planning. We examine how agents perceive their environment through visual, auditory, textual, environmental, and predictive means, breaking down the four-step perception process from sensory input collection to decision-making. The discussion then shifts to how agents use this perceived information to plan their actions, covering goal definition, state representation, action sequencing, and optimization techniques like heuristic search and reinforcement learning. We also explore how different planning frameworks operate and how planning becomes more complex in multi-agent systems where coordination is essential. By understanding these interconnected components, listeners gain insight into what makes AI agents truly intelligent and capable of operating autonomously in complex environments. Find more information at https://www.ibm.com/think/podcasts/techsplainers. Narrated by Selma Pacheco Jimenez