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As AI reshapes how we work, create, and think about our roles, staying adaptable means understanding not just the technology, but our own stories and identities. Reed Frerichs joins Daniel and Chris to explore narrative intelligence, storytelling, leadership, and personal growth in an AI-driven world. They discuss how entrepreneurs and leaders can use AI tools to navigate changing roles, embrace uncertainty, and develop emotional intelligence along with Reed's “own, author, act” framework.
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As AI moves beyond the cloud and into robots, vehicles, and other physical systems, new approaches to models, simulation, and inference are emerging. Daniel and Chris are joined by Ming-Yu Liu, Vice President of Cosmos Lab at NVIDIA, who shares his perspective on open models, world models, and the future of physical AI. They explore why open models matter for innovation, how world models can help AI understand and simulate the physical world, and what it could take to build increasingly capable physical AI systems.
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AI agents are moving beyond laptops and prototypes and into enterprise environments where security, compliance, scalability, and reliability matter. Nick Kuhn from VMware Tanzu Platform joins Daniel and Chris to discuss what changes (and what doesn’t) when deploying agents alongside traditional applications. They explore agent build packs, MCP gateways, shared memory, identity, sandboxing, and what enterprises can learn from years of platform engineering. Nick also shares practical advice for organizations adopting agents today and looks at what the future of AI could mean for enterprise teams and software development.
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AI search is changing how people discover information and how brands need to think about visibility. Daniel and Chris talk with Liam Dunne and Ben Moore, co-founders of Discovered Labs, about the shift from traditional SEO to AI search, what happens behind the scenes when an LLM generates an answer, and why retrieval and citations don't always tell the whole story. They dig into AI visibility, Reddit strategy, query fan-out, representation in model weights, and consensus. They also look ahead to a world where AI agents don't just discover websites, but interact with them and take actions on behalf of users.
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Longtime followers of Practical AI know that multi-repeat guest and friend Demetrios Brinkmann combines brilliant insight and playful banter into one fun-filled show, and this conversion with Chris was no different. They had a blast!
As AI agents become more capable of using computers, interacting with software, and taking action on behalf of users, what does that mean for the way we work and interact with the internet? Chris and Demetrios explore computer-use agents, MCP, agent harnesses, agent-to-agent interactions, and prognosticate the emerging possibilities around agentic commerce. They also discuss how these tools are already being used to automate everyday tasks, the challenges of bringing computer-use agents into enterprise environments, and how the relationship between models and agent harnesses is evolving.
In fact, they had so much fun that their conversation continued long after the show was over, but we didn’t get that here. 🤷♂️
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As AI moves from experimentation to enterprise deployment, are organizations thinking too much about models and not enough about architecture? In this episode, Daniel and Chris talk with Chetan Gupta, Chief AI Officer at Rackspace, about the evolution from industrial AI and physical AI to today’s enterprise AI landscape. Discover how organizations can navigate the complex AI landscape responsibly and effectively as they think about AI architecture, model deployment, AI governance, and AI sovereignty.
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How do we build an AI ecosystem where agents, tools, and systems can work together at scale? Angie Jones, VP of the Agentic AI Foundation, joins Chris to discuss the open standards and projects shaping the agentic future, including MCP, A2A, Goose, etc. They also explore what it takes to drive AI adoption across an entire organization, the importance of neutral standards, global perspectives on agentic AI, and how humans can find the right balance between what they delegate to AI and what they do themselves.
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As AI continues to reshape how organizations work, companies are increasingly asking what AI proficiency should look like across their workforce, and how they can help employees adapt without simply mandating AI adoption. Our returning guest Mike Lewis, Chief AI Architect at TiER1 Performance, joins Dan and Chris to explore AI proficiency through the L0–L3 framework, with a particular focus on the role of non-technical builders. They discuss AI resistance, identifying the right people to build AI-powered solutions, turning tacit knowledge into durable processes, and finding measurable business value from AI.
Mike Lewis was previously on Practical AI episode 289: https://practicalai.show/289
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AI has moved far beyond chatbots, but what exactly are AI models, agents, agent harnesses, and multi-agent systems, and why do they matter?
In this episode, Daniel and Chris break down the terminology behind today's AI landscape, explain the differences between AI features and autonomous agents, and explore why organizations are shifting toward fleets of AI agents powered by multiple models. They also discuss open vs. closed models, enterprise AI architectures, vendor lock-in, and practical ways to begin adopting agentic AI in your own organization.
If you feel left behind by this ongoing agentic AI revolution that has rapidly accelerated to warp speed, then this episode is the catch-up primer you've been waiting for!
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What happens when AI agents driven by a top frontier model escape their secure sandbox? Join Daniel and Chris as they unpack the AI wonk's equivalent of a murder mystery! OpenAI agents went rogue and successfully attacked Hugging Face private infrastructure. Our Dynamic Duo uncover how OpenAI's agents exploited vulnerabilities, moved through networks, and launched a large-scale autonomous attack. They explore what this reveals about agentic AI, cybersecurity, sandboxing, and why organizations need AI systems capable of governing other AI systems. Along the way, Chris and Dan examine the surprising role of open vs. closed models and their link to geopolitics, sovereign AI, and what this incident means for the future of enterprise AI security.
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