Software Engineering Daily

Formal methods are a branch of mathematics and computer science focused on proving the correctness of systems, and they have long promised a more rigorous foundation for software. However, their complexity has kept them confined to a small community of specialists. That is now changing as agentic AI systems take on increasingly autonomous roles. The question of how to define, enforce, and verify what those agents are allowed to do has become urgent, and automated reasoning is emerging as a critical part of the answer.

Byron Cook is a VP and Distinguished Scientist at AWS, a professor at University College London, and a program manager at DARPA. He founded the Automated Reasoning Group at AWS over a decade ago, where his team built the foundations behind products like IAM Access Analyzer, VPC Reachability Analyzer, and Bedrock Guardrails.

In this episode, Byron joins Sean Falconer to discuss how automated reasoning works and why it scales so well with AI, the rise of neurosymbolic approaches that combine formal logic with large language models, what it means to formally specify agent behavior using temporal logic, and why the convergence of agentic AI and formal methods may represent one of the most significant shifts in how software is built and verified.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

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Open source software underpins nearly every modern application, including frameworks powering the most popular websites, to the libraries securing financial backend systems. However, while open source drives collaboration and innovation at a global scale, it also faces deep challenges in sustainability, community health, and long-term maintenance. Many of the world’s most critical dependencies are still maintained by just a handful of volunteers.

Abby Cabunoc Mayes leads Open Source Maintainer Programs at GitHub, and Brian Muenzenmeyer is a Principal Engineer, Node.js maintainer, and author of the book, Approachable Open Source. Abby and Brian join Josh Goldberg to talk about what it means to build and sustain healthy open source projects, how maintainers can foster inclusive communities, the evolving role of open source in the workplace, and how AI is reshaping the way we collaborate.

Josh Goldberg is an independent full time open source developer in the TypeScript ecosystem. He works on projects that help developers write better TypeScript more easily, most notably on typescript-eslint: the tooling that enables ESLint and Prettier to run on TypeScript code. Josh regularly contributes to open source projects in the ecosystem such as ESLint and TypeScript. Josh is a Microsoft MVP for developer technologies and the author of the acclaimed Learning TypeScript (O’Reilly), a cherished resource for any developer seeking to learn TypeScript without any prior experience outside of JavaScript. Josh regularly presents talks and workshops at bootcamps, conferences, and meetups to share knowledge on TypeScript, static analysis, open source, and general frontend and web development.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

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Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score.

Vespa is a popular open source search and data serving engine. Central to Vespa’s architecture is tensor-based retrieval, which is an approach that represents data as tensors rather than simple vectors. Tensor-based retrieval enables richer mathematical operations and more flexible ranking functions that can surmount the limitations of a single vector similarity score.

Radu Gheorghe is a software engineer at Vespa with a background spanning nearly 12 years of consulting and training on Elasticsearch and Solr. In this episode, Radu joins Sean Falconer to discuss why vector similarity alone falls short in production, how tensor-based retrieval generalizes to support richer ranking functions, the trade-offs in chunking and multi-stage re-ranking architectures, and where AI search is headed next.

Full Disclosure: This episode is sponsored by Vespa.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

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SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.

In this episode, they cover Anthropic’s controversial “Mythos” security model and what it means for vulnerability discovery at scale. They also discuss recent layoffs at Snap and Meta, and how AI investment pressures are reshaping hiring, organizational priorities, and the economics of big tech.

Gregor and Sean then zoom out to examine the massive wave of AI infrastructure spending—hundreds of billions in capex across Amazon, Google, Microsoft, and Meta, and what it signals about the future of cloud platforms, model providers, and the engineers who build on top of them. They explore the emerging entanglement between model labs and infrastructure providers, the evolving role of engineers in an AI-native world, and the growing gap between rapid AI adoption and security readiness.

Finally, they highlight standout threads from Hacker News, including creative uses of AI coding tools to revive abandoned side projects, new approaches to training smaller yet highly capable models, surprising demographic data visualizations, and even the mathematics of “cheating” at Tetris.

Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

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AI coding tools have dramatically accelerated the pace of development, and the bottleneck in the software development lifecycle has shifted to code validation and testing. However, the conventional tools and workflows that QA teams have relied on were not designed for a world where a single engineer can generate thousands of lines of code in a day.

SmartBear is a software quality platform spanning test automation, API lifecycle management, and observability. The company recently launched an AI-native QA platform called BearQ, which deploys autonomous agents that explore web applications, learns their structure and behavior, and authors and maintains test cases continuously.

Fitz Nowlan is the VP of AI and Architecture at SmartBear and the co-founder of Reflect, which is a web testing platform acquired by SmartBear in 2024. In this episode, Fitz joins Kevin Ball to discuss why web UI testing is uniquely challenging, how BearQ’s multi-agent architecture coordinates exploration and testing, why test data management becomes a hard distributed systems problem at scale, and what agentic development means for the future of QA.

Full Disclosure: This episode is sponsored by SmartBear.

Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

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Artificial intelligence is transforming warfare faster than the legal and ethical frameworks designed to govern it. Militaries around the world are deploying AI-powered decision support systems to identify targets, assess proportionality, and direct weapons. The gap between what is technically possible and what international law can effectively regulate is widening by the day.

Yuval Shany is a law professor at Hebrew University of Jerusalem and a research fellow at the Oxford Ethics in AI Institute. He also served on the UN Human Rights Committee, where he first encountered the legal and ethical challenges posed by autonomous weapons systems. His research focuses on the intersection of international humanitarian law, human rights, and emerging military technologies.

In this episode, Yuval joins Matt Merrill for a wide-ranging conversation. They cover topics including how close we are to fully autonomous lethal weapons, the accountability gap that AI-mediated warfare creates, and what lessons software engineers can draw from these challenges when building consequential AI systems of any kind.

Matt Merrill is a software engineering leader with over 20 years of experience building and scaling software teams across enterprise and product-focused organizations. His background is in backend development, cloud architecture, and distributed systems design. He currently architects and delivers software products and leads a team of engineers at DEPT® Agency. You can learn more about his work at code.theothermattm.com.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

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Open-weight models are AI systems whose trained parameters are publicly released, which allows developers to run, fine-tune, and deploy them independently rather than accessing them only through a hosted API. While closed-weight models from companies like OpenAI or Anthropic are delivered as managed services, open-weight models give organizations direct control over how the models are deployed and used. Importantly, the performance of these models is steadily improving and they’ve become credible alternatives for production workloads, with advantages in customization and data privacy.

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Fireworks AI is building a platform focused on serving and customizing open-weight models at scale. The platform includes optimized inference infrastructure, multi-hardware support across NVIDIA and AMD, and reinforcement fine-tuning capabilities.

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Benny Chen is a Co-Founder of Fireworks AI. In this episode, he joins Gregor Vand to discuss his path from Meta’s ML infrastructure teams to co-founding Fireworks AI, why open-weight models are becoming increasingly competitive, how custom kernels and speculative decoding improve performance, reinforcement fine-tuning, and much more.

Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

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AI coding tools have gone from novelty to core infrastructure in under three years. Today, many devs use AI daily, a substantial share of new code is AI-generated, and expectations for automation are rapidly increasing.

Sonar is a company specializing in analysis of code quality and security, and they recently released a new survey – the State of Code Developer Survey. The survey provides a deep examination of how developers are using AI in real production environments, and where the real-world gaps and risks still exist.

Chris Grams is the CVP of Corporate Marketing at Sonar, and Manish Kapur is the VP of Product Marketing and Developer Relations at Sonar. In this episode, they join Matt Merrill to discuss what the survey reveals about AI-assisted development, why 96% of developers still don’t fully trust AI-generated code, how deterministic verification layers fit into agent-driven workflows, and what engineering leaders should prioritize as AI shifts from experimentation to production infrastructure.

Sponsorship inquiries: [email protected]

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AI agents are increasingly capable of reasoning and performing autonomous work over long periods. However, as agents take on more complex, longer-horizon tasks, keeping them supplied with the right information becomes the core engineering challenge. The industry is moving away from pre-loading context upfront toward a model where agents dynamically navigate and retrieve the data they need, when they need it.

Redis is approaching context management using a context engine, which is an architecture built around four pillars: on-demand context retrieval, data that is always current, fast retrieval, and a memory layer that improves over time. In practice this means building materialized views of data with a semantic layer on top, rather than giving agents direct access to production databases. A memory system sits alongside this, extracting and compacting information asynchronously as the agent works.

Simba Khadder leads AI strategy at Redis, and he previously co-founded the feature store platform FeatureForm, which was acquired by Redis in 2025. In this episode, Simba joins Kevin Ball to discuss why context has become the defining challenge in agentic AI, how context engines differ from traditional RAG architectures, how materialized views underpin reliable agent data pipelines, how memory systems can improve through async extraction and compaction, and how engineering teams need to adapt their practices as AI-driven development accelerates.

Full Disclosure: This episode is sponsored by Redis.

Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

The post Unlocking the Data Layer for Agentic AI with Simba Khadder appeared first on Software Engineering Daily.

AI agents are evolving from individual productivity tools into distributed systems components inside enterprises. The next frontier is coming into focus, and it involves large-scale ecosystems of collaborating agents embedded directly into business processes. However, multi-agent architectures introduce serious challenges around orchestration, state management, trust, governance, and observability.

Eric Broda is a veteran of the software industry, and he’s the co-author of the new O’Reilly book, Agentic Mesh: The GenAI-Powered Autonomous Agent Ecosystem.

In this episode, Eric joins Sean Falconer to discuss the architectural challenges of deploying agents as core infrastructure, how distributed computing principles apply to multi-agent systems, why trust and explainability are foundational, and what enterprises may look like as agents become full participants in business processes.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.

Please click here to see the transcript of this episode.

Sponsorship inquiries: [email protected]

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