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What does it take to truly observe an AI application? In this episode, we sit down with Giovanni Lima — Open Source Contributor to OpenTelemetry, OpenFeature, and Keptn, and Principal Software Engineer at Dynatrace working on AI Observability. We explore how to instrument AI apps with frameworks like OpenInference, what distributed traces reveal about agent topology, model cost, performance, and accuracy, and why prompt engineering and observability go hand in hand. We also look at emerging trends: internal agentic platforms for back-office automation, and how organizations self-hosting models use observability to optimize hardware, inference, and caching. Links Giovanni's LinkedIn: https://www.linkedin.com/in/giovanni-liva-101a1532/ OpenTelemetry: https://opentelemetry.io/ OTel AI Semantic Conventions: https://github.com/open-telemetry/semantic-conventions-genai OpenInference: https://github.com/Arize-ai/openinference

In the second part of our conversation with Todd, Maintainer of OpenFeature, we learn about why implementing feature flagging is not as easy as at looks at first glance. As a framework developer you need to think about where feature flag evaluations are done. Ideally as close as possible to where your confidential data is you use for making a decision. But that comes with other challenges such as propagating your rule engine potentially to millions of mobile devices. If you think you know all the edge cases then think again and tune in to this episode. Learn about how to not leak any confidential data, how to avoid propagating rules with PII data to millions of devices, and how to build a framework that can evaluate within flags within milliseconds regardless of where your end users execute your code to avoid any impact on end user experience. Links Todd’s LinkedIn: https://www.linkedin.com/in/toddbaert/ OpenFeature: https://openfeature.dev/ Introducing OpenFeature Podcast: https://www.spreaker.com/episode/introducing-openfeature-stepping-into-the-footsteps-of-opentelemetry-with-mike-beemer-and-todd-baert--50043067

Feature flagging is almost as old as software engineering – but only since the increased adoption of AIDLC (AI- Delivery Life Cycle) it has really taking off like AI has taken off as the default way to generate code! In this episode we welcome back Todd Baert, Maintainer of OpenFeature, and learn about the many different use cases of Feature Flagging. Todd explains how feature flagging evolved from simple variables that pivot code execution towards dynamic evaluations that are supported by modern frameworks like OpenFeature and feature flag management systems that are based on that cloud native standard. Tune in and learn what the biggest competitors of OpenFeature are, why feature flagging requires less thinking during development than you think and how it mitigates the risk of AI generated code having negative impact on your systems resiliency! Links Todd’s LinkedIn: https://www.linkedin.com/in/toddbaert/ OpenFeature: https://openfeature.dev/ Introducing OpenFeature Podcast: https://www.spreaker.com/episode/introducing-openfeature-stepping-into-the-footsteps-of-opentelemetry-with-mike-beemer-and-todd-baert--50043067

What happens when your AI coding tools outpace your delivery platform? Andi Grabner and Brian Wilson find out with Michael Binz, DevRel for Juno - the Internal Developer Platform (IDP) at Dynatrace. Together they walk through the full journey of building an IDP that keeps up with AI-speed engineering. Juno serves as a knowledge base, automation hub, and organizational memory across the entire software delivery lifecycle. If your platform can't scale with your AI ambitions, your 10x engineers are working on a 0.1x foundation. This episode shows you what to do about it. Links we discussed: Michaels LinkedIn: https://www.linkedin.com/in/michael-binz-a1307a111/ YouTube Video of their talk: https://www.youtube.com/watch?v=3Ow9rIdv-x8 Bluebox: https://www.bluebox.ai  Max Headroom: https://www.youtube.com/watch?v=egCOO7Zzq0U

Three years ago, Roman Ferstl — entrepreneur, founder, and returning PurePerformance guest — found himself experiencing something he struggled to explain to anyone who hadn't lived it. In this episode, he joins Andi Grabner and Brian Wilson to talk openly about burnout, anxiety, and depression: how they overlap, how they build on each other, and what the science of sleep, the nervous system, and neuroplasticity reveals about why recovery is genuinely possible. Honest about therapy, medication, and mindfulness — what worked and what didn't — Roman's goal is simple: if even one person listening takes one sentence away that changes something for them, the conversation was worth having. You are not broken. And it can get better.

When AI can generate and optimize code at scale, does that make software greener — or just faster to get wrong - or does it cost more to train and run those AI models then the savings we have optimizing our own software? Anne Currie, co-author of O'Reilly's Building Green Software, joins us to untangle the paradox. US data centers are on track to consume over 10% of the national grid by 2030 with a growing part of that energy going to AI workloads, yet it may also be our best tool for writing more efficient software — if the training data is any good (spoiler: for C, it often isn't; for Rust, it's a different story). In our episode Brian, Andi and Anne get into the fundamentals that most teams are skipping: operational efficiency. Turning off systems you don't need, rightsizing what you do — these unglamorous moves can slash your hosting bill in half, and they're the prerequisite for any code-level green gains to actually matter. Plus: graceful shutdowns, grayouts, and why we'll probably need AI to eventually rewrite itself. Links we discussed Anne's LinkedIn: https://www.linkedin.com/in/annecurrie/ Her podcast: https://www.asynchronousunreliable.com/ Chapter 3 of her O'Reilly book: https://www.strategically.green/chapter-3-code-efficiency Full book on Amazon: https://www.amazon.com/dp/1098150627 Brian's story on Myst: https://www.youtube.com/watch?v=EWX5B6cD4_4

AI coding tools are everywhere, but how do you prove they're actually making engineers more productive? In this episode of PurePerformance, hosts Brian Wilson and Andi Grabner welcome Michael Reichenbach, Platform Engineer at 1KOMMA5°, to discuss the company's journey toward more than 80% AI-written code. Rather than relying on anecdotes or hype, Michael shares how his team designed a real experiment to measure the impact of AI-assisted development. We explore the metrics they chose, why traditional DORA metrics such as deployment frequency and change failure rate were not the right indicators, and how they instead focused on "Time to Code" from ticket creation and first commit to merged pull request. Michael also explains how AI lowered the barrier for contribution across the organization, enabling even non-engineering teams to prototype and build solutions faster. The conversation also dives into the operational side of AI adoption, including AI observability dashboards, budget controls, Slack alerts, usage monitoring, and the surprising decision to intentionally limit AI spending during their proof of concept. Whether you're evaluating Cursor, GitHub Copilot, or other AI coding tools, this episode offers practical lessons on measuring value, maintaining quality, and scaling AI adoption responsibly. Links we discussed Michael's LinkedIn: https://www.linkedin.com/in/michael-reichenbach/ Klaus's LinkedIn: https://www.linkedin.com/in/langenheldt/ Talk at Cloud Native Munich: https://www.youtube.com/watch?v=Ntf0h0vFuMQ 1Komm5 Website: https://1komma5.com/ Kenote from KubeCon: https://youtu.be/P1phxZHJGrA?t=570&is=9DYnbK8VGorMmaXo Michael's YouTube Playlist: https://youtube.com/playlist?list=PLn-u2xOcMlXlVweZ0aB4pu6VM6Xv6895i&si=MZ6jzF-VwIAY3HFF

"There is no single way to deploy OpenTelemetry at scale—and that’s exactly the challenge." As organizations adopt OTel across teams and environments, they face tough questions around standardization, configuration, and operating resilient observability pipelines. To address these challenges, the OpenTelemetry community has introduced Blueprints and Reference Implementations—practical guidance on topics like data standards, consistent agent and collector configuration, pipeline resilience, and intelligent sampling. In this episode, we’re joined by Dan Gomez Blanco, maintainer of the OpenTelemetry End-User SIG, to explore real-world reference architectures from organizations like Skyscanner, Adobe, and Mastodon. Tune in to learn how the community is turning OTel complexity into shared best practices—and how you can contribute your own blueprint

I still hear people say, “OpenTelemetry is vendor-neutral, so you can switch any time!” In this episode, Adriana Villela and Josh Lee (both active OpenTelemetry contributors) help bust that myth. While OTel standardizes instrumentation and signal transport—and unlocks a rich ecosystem of tools—switching vendors isn’t as simple as it sounds. There’s real cost in retraining engineers, migrating dashboards, SLOs, and alerts, and reworking deep integrations across your delivery pipeline. We also dive into a key challenge the community is tackling: helping engineers instrument by value, not by default—making it easier to capture the right signals with high quality instead of just collecting everything. Here the links we discussed: Adriana's LinkedIn: https://www.linkedin.com/in/adrianavillela/ Josh's LinkedIn: https://www.linkedin.com/in/joshuamlee/ The blog article: https://thenewstack.io/opentelemetry-vendor-neutrality-guide/ CND Austria Talk: https://www.youtube.com/watch?v=1gxLseuaTdM KCD Prague Talk: https://www.youtube.com/watch?v=pPXG20CXKxQ OpenTelemetry Project Website: https://opentelemetry.io/

In this episode, we explore how AI is transforming education, from classrooms to corporate training. What changes are needed in schools and universities? How does AI affect both students and educators? And how should companies rethink internal training and hiring to stay competitive? To answer these questions, we’re joined by Rainer Stropek, CEO of Software Architects and Chairman of Coding Club Linz. With decades of experience teaching at high schools and universities—and helping organizations upskill their engineers—Rainer brings a unique perspective on how software engineering education is evolving. While many view AI as a threat, Rainer sees it as a “Christmas gift”—opening up endless opportunities to learn, adapt, and innovate. Tune in to hear why curiosity is more important than ever, how educational institutions can prepare future engineers, and why organizations must step up to ensure everyone has a fair chance to succeed in the age of AI. Links we discussed Rainer's LinkedIn Profile: https://www.linkedin.com/in/rainerstropek/ Rainer's Website: https://rainerstropek.me/ CodeClub: https://codeclub.org/en/ Coder DoJo Linz: https://linz.coderdojo.net/

Its rare - but it happens: A guest-free episode of PurePerformance, allowing Andi Grabner and Brian Wilson reconnect to share real-world insights from recent months in the cloud-native and observability space. From KubeCon Amsterdam experiences and the strength of open-source collaboration to emerging challenges like AI-generated contributions, they explore how the industry is evolving beyond the hype. Your co-hosts of PurePerformance discuss the changing role of observability in the AI-native era—both as a foundation for understanding complex systems and as a tool to monitor AI itself. Brian shares his personal shift from AI skepticism to practical adoption, highlighting how AI can significantly improve productivity when used thoughtfully. Hope you all enjoy this episode!

In 2011 Heroku defined the 12 factor app to remove emerging bottlenecks as developers tried to scale their output when they moved from building monoliths to microservices. In Platform Engineer we see a repeating pattern called the "8 Factor Platform Producers".  AI allows engineering teams to speed up but they face bottlenecks as platform capabilities are not scaling with that demand as they are often depending on a central platform engineering team to be built and maintained. To learn more about 8 Factor Platform Producers we invited Abby Bangser,  Founding Principal Engineer at Syntasso and CNCF Ambassador. She gave an amazing talk at KubeCon in Amsterdam and today walks us through the need of defining both consumers and producers for platforms to eliminate any emerging bottlenecks in Platform Engineering and allow an organization to reap the benefit of speeding up with AI Links we discussed: Abby's LinkedIn: https://www.linkedin.com/in/abbybangser/ Abby's Kubecon Keynote: https://www.youtube.com/watch?v=8t0-5cvvMGM&list=PLj6h78yzYM2MXCOWSN9CqqID6OOvF7wxL&index=30 12 Factor Apps: https://12factor.net/ CNCF Whitepaper: https://cloudnativeplatforms.com/whitepapers/platforms/

As the software world is transforming from cloud native to AI-native, observability must transform with it. But how exactly? How do we apply this in an existing enterprise with established processes and practices? In this PurePerformance episode, Andi Grabner hosts Hilliary Lipsig and Rob Rati to discuss their new book, Observability in the AI‑Native Era. The conversation explores how AIOps, automation, and modern observability must evolve as systems become cloud‑native, data‑heavy, and AI‑driven. We talk about why old alerting and SLO models no longer scale, how to balance AI with automation and human judgment, and why trust, security, and compliance matter more than ever when machines start making operational decisions. A must‑listen for SREs, platform engineers, and engineering leaders navigating the AI‑native future. Links we discussed Book on Amazon: https://www.amazon.com/Observability-AI-Native-Era-Artificial-Intelligence-ebook/dp/B0GHZH1YFL Hilliary LinkedIn: https://www.linkedin.com/in/hilliary-lipsig-a5935245/ Rob LinkedIn: https://www.linkedin.com/in/roberthrati/ Andi LinkedIn: https://www.linkedin.com/in/grabnerandi/

AI coding agents are fast—but speed alone doesn’t guarantee quality. In this episode, Andi Grabner talks with Lukas Holzer (Straion) about why large context files and “almost right” AI code create new risks for engineering teams. You will learn about the "Lost in the Middle Syndrom" and why many organizations are not getting the promised 10x engineering boost right now! Andi and Lukas also explore rule adherence, dynamic context generation, enterprise readiness for AI-first development, and how software engineering roles are evolving in the age of AI. Tune in to learn more ... Links we discussed LinkedIn Profile: https://www.linkedin.com/in/lukas-holzer/ Straion Website: https://straion.com/ 90 Percent Rule Blog: https://straion.com/blog/90-percent-rule-adherence-straion-coding-agents/ 1million tokens Blog: https://straion.com/blog/1m-tokens-wont-save-your-engineering-standards/

In this episode of the PurePerformance Podcast, Andi and Brian sit down with Chris LaBrado—Solutions Architect for AI Enablement, FSO, SRE, and ITSM at HSN/QVC, where he has spent an incredible 27 years shaping operational excellence. Their conversation dives deep into how AI is transforming software creation, enterprise workflows, and even the very role of developers. Chris shares how the barrier to entry for building tools and automation has dropped overnight thanks to natural‑language-based development: “Everyone can now create automation or tools without having to worry about the syntax.” He explains why AI is rapidly becoming the primary interface into the enterprise—capable of navigating presentations, emails, and complex back‑office systems—and why the future of engineering may shift from human‑oriented coding to AI-driven development models such as MDCD (MarkDown Continuous Development). The discussion also takes unexpected but fascinating detours into Chris’s background as a former bowling‑industry podcaster, his recent work with generative agents like DynaClaude, his Vibe Coded Root Cause Agent, and a philosophical exploration of AI, creativity, and the concept of singularity. Amidst all the change, Chris remains optimistic: “AI opens up a lot of new opportunity for everyone willing to adapt. It will result in us creating more things that ultimately help us as humans.” This episode is a thoughtful, energizing look at where software engineering is headed—and why the future might be brighter than we think. Links we discussed Chris LaBrado on LinkedIn: https://www.linkedin.com/in/chrislabrado/ Mo Gawdat, former Google Executive on the Singularity "moment of truth": https://x.com/vitrupo/status/2008824930646057380?s=20 CEO of NVIDIA had an interesting excerpt from interview: https://x.com/MinusWells/status/2031974516155695414?s=20 Elon Musk on speed of AI: https://x.com/r0ck3t23/status/2031639621465931903?s=20 AI brain emulation of a fly (e.g. "a sign of the times"): https://x.com/alexwg/status/2030217301929132323?s=20 Elon on fiat currency transforming based on AI manufacturing loop: https://x.com/elonmusk/status/2020202496547844312?s=20 Fiat currency moves to model based on thermodynamics: https://x.com/r0ck3t23/status/2033371028202602547?s=20