AI Engineering Podcast

• From MRI to World Models: How AI Is Changing What We See

  SummaryIn this episode of the AI Engineering Podcast Daniel Sodickson, Chief of Innovation in Radiology at NYU Grossman School of Medicine, talks about harnessing AI systems to truly understand images and revolutionize science and healthcare. Dan shares his journey from linear reconstruction to early deep learning for accelerated MRI, highlighting the importance of domain expertise when adapting models to specialized modalities. He explores "upstream" AI that changes what and how we measure, using physics-guided networks, prior knowledge, and personal baselines to enable faster, cheaper, and more accessible imaging. The conversation covers multimodal world models, cross-disciplinary translation, explainability, and a future where agents flag abnormalities while humans apply judgment, as well as provocative frontiers like "imaging without images," continuous health monitoring, and decoding brain activity. Dan stresses the need to preserve truth, context, and human oversight in AI-driven imaging, and calls for tools that distill core methodologies across disciplines to accelerate understanding and progress.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsWhen ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.Unlock the full potential of your AI workloads with a seamless and composable data infrastructure. Bruin is an open source framework that streamlines integration from the command line, allowing you to focus on what matters most - building intelligent systems. Write Python code for your business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. With native support for ML/AI workloads, Bruin empowers data teams to deliver faster, more reliable, and scalable AI solutions. Harness Bruin's connectors for hundreds of platforms, including popular machine learning frameworks like TensorFlow and PyTorch. Build end-to-end AI workflows that integrate seamlessly with your existing tech stack. Join the ranks of forward-thinking organizations that are revolutionizing their data engineering with Bruin. Get started today at aiengineeringpodcast.com/bruin, and for dbt Cloud customers, enjoy a $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Daniel Sodickson about the impact and applications of AI that is capable of image understandingInterviewIntroductionHow did you get involved in machine learning?Images and vision are concepts that we understand intuitively, but which have a large potential semantic range. How would you characterize the scope and application of imagery in the context of AI and other autonomous technologies?Can you give an overview of the current state of image/vision capabilities in AI systems?A predominant application of machine vision has been for object recognition/tracking. How are advances in AI changing the range of problems that can be solved with computer vision systems?A substantial amount of work has been done on processing of images such as the digital pictures taken by smartphones. As you move to other types of image data, particularly in non-visible light ranges, what are the areas of similarity and in what ways do we need to develop new processing/analysis techniques?What are some of the ways that AI systems will change the ways that we conceive of What are the most interesting, innovative, or unexpected ways that you have seen AI vision used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on imaging technologies and techniques?When is AI the wrong choice for vision/imaging applications?What are your predictions for the future of AI image understanding?Contact InfoLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksMRI == Magnetic Resonance ImagingLinear AlgorithmNon-Linear AlgorithmCompressed SensingDictionary Learning AlgorithmDeep LearningCT ScanCambrian ExplosionLIDAR Point CloudSynthetic Aperture RadarGeoffrey HintonCo-Intelligence by Ethan Mollick (affiliate link)TomographyX-Ray CrystallographyCERNCLIP ModelPhysics-Guided Neural NetworkFunctional MRIA Path Toward Autonomous Machine Intelligence by Yann LeCunThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

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• Specs, Tests, and Self‑Verification: The Playbook for Agentic Engineering Teams

  SummaryIn this episode Andrew Filev, CEO and founder of ZenCoder, takes a deep dive into the system design, workflows, and organizational changes behind building agentic coding systems. He traces the evolution from autocomplete to truly agentic models, discusses why context engineering and verification are the real unlocks for reliability, and outlines a pragmatic path from “vibe coding” to AI‑first engineering. Andrew shares ZenCoder’s internal playbook: PRD and tech spec co‑creation with AI, human‑in‑the‑loop gates, test‑driven development, and emerging BDD-style acceptance testing. He explores multi-repo context, cross-service reasoning, and how AI reshapes team communication, ownership, and architecture decisions. He also covers cost strategies, when to choose agents vs. manual edits, and why self‑verification and collaborative agent UX will define the next wave. Andrew offers candid lessons from building ZenCoder—why speed of iteration beats optimizing for weak models, how ignoring the emotional impact of vibe coding slowed brand momentum, and where agentic tools fit across greenfield and legacy systems. He closes with predictions for the next year: self‑verification, parallelized agent workflows, background execution in CI, and collaborative spec‑driven development moving code review upstream.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsWhen ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.Your host is Tobias Macey and today I'm interviewing Andrew Filev about the system design and integration strategies behind building coding agents at ZencoderInterviewIntroductionHow did you get involved in ML/AI?There have been several iterations of applications for generative AI models in the context of software engineering. How would you characterize the different approaches or categories?Over the course of this summer (2025) the term "vibe coding" gained prominence with the idea that the human just needs to be worried about whether the software does what you ask, not how it is written. How does that sentiment compare to your philosophies on the role of agentic AI in the lifecycle of software?This points at a broader challenge for software engineers in the AI era; how much control can and should we cede to the LLMs, and over what elements of the software process?This also brings up useful questions around the experience of the engineer collaborating with the agent. What are the different interaction patterns that individuals and teams should be thinking of in their use of AI engineering tools?Should the agent be proactive? reactive? what are the triggers for an action to be taken and to what extent?What differentiates a coding agent from an agentic editor?The key challenge in any agent system is context engineering. Software is inherently structured and provides strong feedback loops. But it can also be very messy or difficult to encapsulate in a single context window. What are some of the data structures/indexing strategies/retrieval methods that are most useful when providing guidance to an agent?Software projects are rarely fully self-contained, and often need to cross repository boundaries, as well as manage dependencies. What are some of the more challenging aspects of identifying and accounting for those sometimes implicit relationships?What are some of the strategies that are most effective for yielding productive results from an agent in terms of prompting and scoping of the problem?What are some of the heuristics that you use to determine whether and how to employ an agent for a given task vs. doing it manually?How can the agents assist in the decomposition and planning of complex projects?What are some of the ways that single-player interaction strategies can be turned into team/multi-player strategies?What are some of the ways that teams can create and curate productive patterns to accelerate everyone equally?What are the most interesting, innovative, or unexpected ways that you have seen coding agents used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on coding agents at Zencoder?When is/are Zencoder/coding agents the wrong choice?What do you have planned for the future of Zencoder/agentic software engineering?Contact InfoLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksZencoderWrikeDARPA Robotics ChallengeCognitive ComputingAndrew NgSebastian ThrunGithub CopilotRAG == Retrieval Augmented GenerationRe-rankingClaude Sonnet 3.5SWE-BenchVibe CodingAI First EngineeringWaterfall Software EngineeringAgile Software EngineeringPRD == Project Requirements DocumentBDD == Behavior-Driven DevelopmentVSCodeThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

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• From Probabilistic to Trustworthy: Building Orion, an Agentic Analytics Platform

  SummaryIn this episode of the AI Engineering Podcast Lucas Thelosen and Drew Gillson talk about Orion, their agentic analytics platform that delivers proactive, push-based insights to business users through asynchronous thinking with rich organizational context. Lucas and Drew share their approach to building trustworthy analysis by grounding in semantic layers, fact tables, and quality-assurance loops, as well as their focus on accuracy through parallel test-time compute and evolving from probabilistic steps to deterministic tools. They discuss the importance of context engineering, multi-agent orchestration, and security boundaries for enterprise deployments, and share lessons learned on consistency, tool design, user change management, and the emerging role of "AI manager" as a career path. The conversation highlights the future of AI knowledge workers collaborating across organizations and tools while simplifying UIs and raising the bar on actionable, trustworthy analytics.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsWhen ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.Your host is Tobias Macey and today I'm interviewing Lucas Thelosen and Drew Gillson about their experiences building an agentic analytics platform and the challenges of ensuring accuracy to build trustInterviewIntroductionHow did you get involved in machine learning?Can you describe what Orion is and the story behind it?Business analytics is a field that requires a high degree of accuracy and detail because of the potential for substantial impact on the business (positive and negative). These are areas that generative AI has struggled with achieving consistently. What was your process for building confidence in your ability to achieve that threshold before committing to the path you are on now?There are numerous ways that generative AI can be incorporated into the process of designing, building, and delivering analytical insights. How would you characterize the different strategies that data teams and vendors have approached that problem?What do you see as the organizational benefits of moving to a push-based model for analytics?Can you describe the system architecture of Orion?Agentic design patterns are still in the early days of being developed and proven out. Can you give a breakdown of the approach that you are using?How do you think about the responsibility boundaries, communication paths, temporal patterns, etc. across the different agents?Tool use is a key component of agentic architectures. What is your process for identifying, developing, validating, and securing the tools that you provide to your agents?What are the boundaries and extension points that you see when building agentic systems? What are the opportunities for using e.g. A2A for protocol for managing agentic hand-offs?What is your process for managing the experimentation loop for changes to your models, data, prompts, etc. as you iterate on your product?What are some of the ways that you are using the agents that power your system to identify and act on opportunities for self-improvement?What are the most interesting, innovative, or unexpected ways that you have seen Orion used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Orion?When is an agentic approach the wrong choice?What do you have planned for the future of Orion?Contact InfoLucasLinkedInDrewLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksGravityOrion Data Engineering Podcast EpisodeSite Reliability EngineeringAnthropic Claude Sonnet 4.5A2A (Agent2Agent) ProtocolSimon WillisonAI Lethal TrifectaBehavioral ScienceGrounded TheoryLLM as a JudgeRLHF == Reinforcement Learning from Human FeedbackThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

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• Building Production-Ready AI Agents with Pydantic AI

  SummaryIn this episode of the AI Engineering Podcast Samuel Colvin, creator of Pydantic and founder of Pydantic Inc, talks about Pydantic AI - a type-safe framework for building structured AI agents in Python. Samuel explains why he built Pydantic AI to bring FastAPI-like ergonomics and production-grade engineering to agents, focusing on strong typing, minimal abstractions, and reliability, observability, and stability. He explores the evolving agent ecosystem, patterns for single vs. many agents, graphs vs. durable execution, and how Pydantic AI approaches structured I/O, tool calling, and MCP with type safety in mind. Samuel also shares insights on design trade-offs, model-provider churn, schema unification, safe code execution, security gaps, and the importance of open standards and OpenTelemetry for observability.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsWhen ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.Your host is Tobias Macey and today I'm interviewing Samuel Colvin about the Pydantic AI framework for building structured AI agentsInterviewIntroductionHow did you get involved in machine learning?Can you describe what Pydantic AI is and the story behind it?What are the core use cases and capabilities that you are focusing on with PydanticAI?The agent SDK landscape has been incredibly crowded and volatile since the introduction of LangChain and LlamaIndex. Can you give your summary of the current state of the ecosystem?What are the broad categories that you use when evaluating the various frameworks?Beyond the volatility of the frameworks, there is also a rapid pace of evolution in the different styles/patterns of agents. What are the patterns and integrations that Pydantic AI is best suited for?Can you describe the overall design/architecture of the Pydantic AI framework?How have the design and scope evolved since you first started working on it?For someone who wants to build a sophisticated, production-ready AI agent with Pydantic AI, what is your recommended path from idea to deployment?What are the elements of the framework that help engineers across those different stages of the lifecycle?What are some of the key learnings that you gained from all of your efforts on Pydantic that have been most helpful in developing and promoting Pydantic AI?What are some of the new and exciting failure modes that agentic applications introduce as compared to web/mobile/scientific/etc. applications?What are the most interesting, innovative, or unexpected ways that you have seen Pydantic AI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pydantic AI?When is Pydantic AI the wrong choice?What do you have planned for the future of Pydantic AI?Contact InfoGitHubLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksPydanticPydantic AIPydantic IncPydantic LogfireOpenAI AgentsGoogle ADKLangChainLlamaIndexCrewAIDurable ExecutionTemporalMCP == Model Context ProtocolClaude CodeTypescriptGemini Structured OutputOpenAI Structured OutputDottxt Outlines SDKsmolagentsLiteLLMOpenRouterOpenAI Responses APIFastAPISQLModelAI SDK JavaScriptLangGraphNextJSPyodideAI Elements frontend component libraryThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

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• From GPUs to Workloads: Flex AI’s Blueprint for Fast, Cost‑Efficient AI

  SummaryIn this episode of the AI Engineering Podcast Brijesh Tripathi, CEO of Flex AI, talks about revolutionizing AI engineering by removing DevOps burdens through "workload as a service". Brijesh shares his expertise from leading AI/HPC architecture at Intel and deploying supercomputers like Aurora, highlighting how access friction and idle infrastructure slow progress. He discusses Flex AI's innovative approach to simplifying heterogeneous compute, standardizing on consistent Kubernetes layers, and abstracting inference across various accelerators, allowing teams to iterate faster without wrestling with drivers, libraries, or cloud-by-cloud differences. Brijesh also shares insights into Flex AI's strategies for lifting utilization, protecting real-time workloads, and spanning the full lifecycle from fine-tuning to autoscaled inference, all while keeping complexity at bay.AnnouncementsHello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsWhen ML teams try to run complex workflows through traditional orchestration tools, they hit walls. Cash App discovered this with their fraud detection models - they needed flexible compute, isolated environments, and seamless data exchange between workflows, but their existing tools couldn't deliver. That's why Cash App rely on Prefect. Now their ML workflows run on whatever infrastructure each model needs across Google Cloud, AWS, and Databricks. Custom packages stay isolated. Model outputs flow seamlessly between workflows. Companies like Whoop and 1Password also trust Prefect for their critical workflows. But Prefect didn't stop there. They just launched FastMCP - production-ready infrastructure for AI tools. You get Prefect's orchestration plus instant OAuth, serverless scaling, and blazing-fast Python execution. Deploy your AI tools once, connect to Claude, Cursor, or any MCP client. No more building auth flows or managing servers. Prefect orchestrates your ML pipeline. FastMCP handles your AI tool infrastructure. See what Prefect and Fast MCP can do for your AI workflows at aiengineeringpodcast.com/prefect today.Your host is Tobias Macey and today I'm interviewing Brijesh Tripathi about FlexAI, a platform offering a service-oriented abstraction for AI workloadsInterviewIntroductionHow did you get involved in machine learning?Can you describe what FlexAI is and the story behind it?What are some examples of the ways that infrastructure challenges contribute to friction in developing and operating AI applications?How do those challenges contribute to issues when scaling new applications/businesses that are founded on AI?There are numerous managed services and deployable operational elements for operationalizing AI systems. What are some of the main pitfalls that teams need to be aware of when determining how much of that infrastructure to own themselves?Orchestration is a key element of managing the data and model lifecycles of these applications. How does your approach of "workload as a service" help to mitigate some of the complexities in the overall maintenance of that workload?Can you describe the design and architecture of the FlexAI platform?How has the implementation evolved from when you first started working on it?For someone who is going to build on top of FlexAI, what are the primary interfaces and concepts that they need to be aware of?Can you describe the workflow of going from problem to deployment for an AI workload using FlexAI?One of the perennial challenges of making a well-integrated platform is that there are inevitably pre-existing workloads that don't map cleanly onto the assumptions of the vendor. What are the affordances and escape hatches that you have built in to allow partial/incremental adoption of your service?What are the elements of AI workloads and applications that you are explicitly not trying to solve for?What are the most interesting, innovative, or unexpected ways that you have seen FlexAI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on FlexAI?When is FlexAI the wrong choice?What do you have planned for the future of FlexAI?Contact InfoLinkedInParting QuestionFrom your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?LinksFlex AIAurora Super ComputerCoreWeaveKubernetesCUDAROCmTensor Processing Unit (TPU)PyTorchTritonTrainiumASIC == Application Specific Integrated CircuitSOC == System On a ChipLoveableFlexAI BlueprintsTenstorrentThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

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