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.

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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.

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Narrated by Alice Gomstyn

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 of AI, including conversational AI, prompts, recommendations, and tuning for assistants, and autonomy, decision-making, connectivity, persistent memory, and adaptive learning for agents. Real-world applications and potential benefits of both technologies are highlighted, along with current limitations and risks.

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Narrated by Alice Gomstyn

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 operations: goal initialization and planning, reasoning with available tools, and learning and reflection. We also contrast agentic AI chatbots with nonagentic ones, highlighting the advantages of adaptability and comprehensive responses in agentic systems. Finally, we examine reasoning paradigms, agent types, and various use cases, from enhancing customer experiences to revolutionizing healthcare and finance.

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

Narrated by Alice Gomstyn

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, there are still some challenges, such as misaligned rewards, self-reinforcing behaviors, and cascading failures. Examples of real-world applications are provided, such as AI-powered trading bots, autonomous vehicles, healthcare chatbots, cybersecurity, and supply chain management. 

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

Narrated by Alice Gomstyn

Welcome to Techsplainers by IBM, your daily dose of AI and technology insights from Monday to Friday, produced by IBM. Every weekday, we explore a new trending topic, such as generative AI, agentic AI, cybersecurity, data for AI, and more. With hundreds of topics to explore, there’s always something new to learn. Perfect for your commute, workout, or coffee break.  

Have a topic that you want techsplained? Let us know in the comments! Tune in every weekday at 6 AM ET to explore new topics, stay ahead, and learn smarter. 

Visit the podcast page: https://www.ibm.com/think/podcasts/techsplainers 

Learn more about tech topics: https://www.ibm.com/think/topics