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AI Engineering Podcast

AI Engineering Podcast

Di: Tobias Macey
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 This show is your guidebook to building scalable and maintainable AI systems. You will learn how to architect AI applications, apply AI to your work, and the considerations involved in building or customizing new models. Everything that you need to know to deliver real impact and value with machine learning and artificial intelligence.© 2024 Boundless Notions, LLC.
Episodi
  • The Future of Dev Experience: Spotify’s Playbook for Organization‑Scale AI

    The Future of Dev Experience: Spotify’s Playbook for Organization‑Scale AI
    Jan 20 2026
    Summary In this episode of the AI Engineering Podcast Niklas Gustavsson, Chief Architect at Spotify, talks about scaling AI across engineering and product. He explores how Spotify's highly distributed architecture was built to support rapid adoption of coding agents like Copilot, Cursor, and Claude Code, enabled by standardization and Backstage. The conversation covers the tension between bottoms-up experimentation and platform standardization, and how Spotify is moving toward monorepos and fleet management. Niklas discusses the emergence of "fleet-wide agents" that can execute complex code changes with robust testing and LLM-as-judge loops to ensure quality. He also touches on the shift in engineering workflows as code generation accelerates, the growing use of agents beyond coding, and the lessons learned in sandboxing, agent skills/rules, and shared evaluation frameworks. Niklas highlights Spotify's decade-long experience with ML product work and shares his vision for deeper end-to-end integration of agentic capabilities across the full product lifecycle and making collaborative "team-level memory" for agents a reality. Announcements Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systemsUnlock 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 Niklas Gustavsson about how Spotify is scaling AI usage in engineering and product workInterview IntroductionHow did you get involved in machine learning?Can you start by giving an overview of your engineering practices independent of AI?What was your process for introducing AI into the developmer experience? (e.g. pioneers doing early work (bottom-up) vs. top-down)There are countless agentic coding tools on the market now. How do you balance organizational standardization vs. exploration?Beyond the toolchain, what are your methods for sharing best practices and upskilling engineers on use of agentic toolchains for software/product engineering?Spotify has been operationalizing ML/AI features since before the introduction of LLMs and transformer models. How has that history helped inform your adoption of generative AI in your overall engineering organization?As you use these generative and agentic AI utilities in your day-to-day, how have those lessons learned fed back into your AI-powered product features?What are some of the platform capabilities/developer experience investments that you have made to improve the overall effectiveness of agentic coding in your engineering organization?What are some examples of guardrails/speedbumps that you have introduced to avoid injecting unreliable or untested work into production?As the (time/money/cognitive) cost of writing code drops that increases the burden on reviewing that code. What are some of the ways that you are working to scale that side of the equation?What are some of the ways that agentic coding/CLI utilities have bled into other areas of engineering/opertions/product development beyond just writing code?What are the most interesting, innovative, or unexpected ways that you have seen your team applying AI/agentic engineering practices?What are the most interesting, unexpected, or challenging lessons that you have learned while working on operationalizing and scaling agentic engineering patterns in your teams?When is agentic code generation the wrong choice?What do you have planned for the future of AI and agentic coding patterns and practices in your organization?Contact Info LinkedInParting Question From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?Closing Announcements Thank 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,...
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    56 min
  • Generative AI Meets Accessibility: Benchmarks, Breakthroughs, and Blind Spots with Joe Devon

    Generative AI Meets Accessibility: Benchmarks, Breakthroughs, and Blind Spots with Joe Devon
    Jan 5 2026
    Summary In this episode Joe Devon, co-founder of Global Accessibility Awareness Day (GAAD), talks about how generative AI can both help and harm digital accessibility — and what it will take to tilt the balance toward inclusion. Joe shares his personal motivation for the work, real-world stakes for disabled users across web, mobile, and developer tooling, and compelling stories that illustrate why accessible design is a human-rights issue as much as a compliance checkbox. He digs into AI’s current and future roles: from improving caption quality and auto-generating audio descriptions to evaluating how well code-gen models produce accessible UI by default. Joe introduces AIMAC (AI Model Accessibility Checker), a new benchmark comparing top models on accessibility-minded code generation, what the results reveal, and how model providers and engineering teams can practically raise the bar with linters, training data, and cultural change. He closes with concrete guidance for leaders, why involving people with disabilities is non-negotiable, and how solving for edge cases makes AI—and products—better for everyone. Announcements Hello 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 Joe Devon about opportunities for using generative AI to improve the accessibility of digital technologiesInterview IntroductionHow did you get involved in AI?Can you starty by giving an overview of what is included in the term "accessibility"?What are some of the major contributors to a lack of accessibility in digital experiences today?Beyond the web, what are some of the other platforms and interfaces that struggle with accessibility?What role does/can generative AI utilities play in improving the accessibility of applications?You recently helped create the AI Model Accessibility Checker (AIMAC) to benchmark which coding agents produce the most accessible code. What are the goals of that project and desired outcomes from its introduction?What were the key findings from AIMAC's initial benchmarking results? Were there any surprises in terms of which models performed better or worse at generating accessible code?The automation offered by using agentic software development toolchains reduces the manual effort involved in building accessible interfaces. What are the opportunities for using generative AI utilities to act as an assistive mechanism for existing sites/technologies?Beyond code generation, what other aspects of the AI development lifecycle need accessibility considerations - training data, model outputs, user interfaces for AI tools themselves?You co-host the Accessibility and Gen AI Podcast. What are some of the common misconceptions you encounter about AI's role in ...
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    56 min
  • Beyond the Chatbot: Practical Frameworks for Agentic Capabilities in SaaS

    Beyond the Chatbot: Practical Frameworks for Agentic Capabilities in SaaS
    Dec 29 2025
    Summary In this episode product and engineering leader Preeti Shukla explores how and when to add agentic capabilities to SaaS platforms. She digs into the operational realities that AI agents must meet inside multi-tenant software: latency, cost control, data privacy, tenant isolation, RBAC, and auditability. Preeti outlines practical frameworks for selecting models and providers, when to self-host, and how to route capabilities across frontier and cheaper models. She discusses graduated autonomy, starting with internal adoption and low-risk use cases before moving to customer-facing features, and why many successful deployments keep a human-in-the-loop. She also covers evaluation and observability as core engineering disciplines - layered evals, golden datasets, LLM-as-a-judge, path/behavior monitoring, and runtime vs. offline checks - to achieve reliability in nondeterministic systems. Announcements Hello 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 Preeti Shukla about the process for identifying whether and how to add agentic capabilities to your SaaSInterview IntroductionHow did you get involved in machine learning?Can you start by describing how a SaaS context changes the requirements around the business and technical considerations of an AI agent?Software-as-a-service is a very broad category that includes everything from simple website builders to complex data platforms. How does the scale and complexity of the service change the equation for ROI potential of agentic elements?How does it change the implementation and validation complexity?One of the biggest challenges with introducing generative AI and LLMs in a business use case is the unpredictable cost associated with it. What are some of the strategies that you have found effective in estimating, monitoring, and controlling costs to avoid being upside-down on the ROI equation?Another challenge of operationalizing an agentic workload is the risk of confident mistakes. What are the tactics that you recommend for building confidence in agent capabilities while mitigating potential harms?A corollary to the unpredictability of agent architectures is that they have a large number of variables. What are the evaluation strategies or toolchains that you find most useful to maintain confidence as the system evolves?SaaS platforms benefit from unit economics at scale and often rely on multi-tenant architectures. What are the security controls and identity/attribution mechanisms that are critical for allowing agents to operate across tenant boundaries?What are the most interesting, innovative, or unexpected ways that you have seen SaaS products adopt agentic patterns?What are the most interesting, ...
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    54 min
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