Most AI agent setups today are built around a single session, where one user interacts with one agent at a time. However, that model breaks down when an agent has to serve a business, where thousands of requests can arrive at once and each session needs to be isolated, durable, and recoverable. Getting agents to run reliably at that scale has meant a lot of hand-rolled infrastructure beneath the agent itself.

eve is an open source, cloud-native agent framework from Vercel that removes much of the agent scaling burden. In the eve framework, an agent is defined declaratively through configuration files and these files compile into infrastructure as code so the platform provisions only what the agent actually uses.

Andrew Barba is a Member of Technical Staff at Vercel, and Shar Dara is the Product Lead for eve at Vercel. In this episode, they join Kevin Ball to discuss what it means for an agent framework to be cloud-native, why they chose to express agents in plain English, and their view that company building is becoming agent building.

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Historically, databases were responsible for storing data and returning exact results in response to queries. However, AI is now bending that contract in a new direction. Applications increasingly expect structured and unstructured data to come together. This is pushing databases into territory that looks more like search, where relevance and ranking matter and results are no longer strictly exact. Agents are also beginning to write their own queries and even propose their own schemas, which raises new questions about how data should be structured, governed, and trusted.

Sailesh Krishnamurthy is a VP of Engineering at Google, and in this episode he joins Matt Merrill to discuss his background, how databases have evolved over the past fifty years, and where the field is heading as AI reshapes how data is queried, structured, and trusted.

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A Rust Framework to Simplify Distributed Systems

Building software that runs across many machines is notoriously difficult. Developers have to grapple with problems such as race conditions, partial failures, and message ordering. Notably, one category of distributed software has largely escaped these burdens. A distributed database can spread a single query across thousands of machines, handling the coordination, failure recovery, and ordering internally. This raises a natural question of why general-purpose distributed programming can’t feel the same way.

This is a highly practical problem at AWS, because the reliability of cloud infrastructure depends on getting distributed systems right at massive scale. Joe Hellerstein spent thirty years as a database and distributed systems researcher at UC Berkeley, where he pioneered much of the foundational thinking on applying database ideas to distributed programming. He is now at AWS, where he works to bring his research into production through Hydro, which is a Rust framework to bring declarative queries to general-purpose distributed programming.

In this episode, Joe joins Sean Falconer to discuss how ideas from the database world could make distributed programming dramatically simpler and safer.

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

In this end-of-summer episode, Gregor and Sean turn to a busy season of mergers and acquisitions, including NVIDIA‘s reported $12.9 billion acquisition of Hugging Face, Dynatrace‘s near-billion-dollar deal for AI observability platform Arize, and Temporal‘s rumored raise at a $12 billion valuation. They also cover a run of large funding rounds, from AI security startup HiddenLayer to AI personal assistant Instinct and restaurant software platform Owner.com, all against the backdrop of a deluge of new model releases.

The main topic digs into China’s “transfer station” economy, the sprawling proxy market that gives developers cheap access to frontier models officially banned in the country. Drawing on a China Talk report, they walk through the tactics at play, from “one fish, three meals” credit farming to silent model swapping, and unpack how this pipeline of captured outputs and human traces may be fueling the recent surge in high-quality open weight models. Gregor and Sean also examine where Meta now sits in the landscape after shelving Llama and pivoting toward its Muse family.

As always, the episode wraps up with a few standout Hacker News threads, including a critique of how log-scale charts obscure the real cost gap between open weight and frontier models, and a look at “invisible companies” as an under-the-radar investment strategy.

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Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question.

Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently developed Nexus, which is a knowledge engine that reframes context as a first-class, precomputed asset rather than something reassembled on the fly. The approach borrows the database concept of a materialized view, and curates context once into a versioned artifact that carries its own schema, metadata, permissions, and lineage.

Jörg Schad is the VP of Engineering at Pinecone. In this episode, he joins Kevin Ball for an in-depth conversation about the frontier of retrieval technology. They discuss precompiled context, how context artifacts are curated and versioned much like code, how metadata and semantic layers help agents choose the right information, and much more.

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Age verification is reshaping how people access the internet. An ever-growing patchwork of laws can now require government IDs, facial age estimation, or behavioral inference before you can enter digital spaces. Discord, app stores, social media platforms, and AI chatbots are all being pulled into these requirements. Almost always, these measures are enacted under the banner of protecting children. However, beneath that goal lies a set of hard questions about privacy, anonymity, security, and the line between what a private platform may choose to do and what a government may compel.

John Coleman is a legislative counsel at the Foundation for Individual Rights and Expression, or FIRE, where he focuses on artificial intelligence and free speech policy. In this episode, John joins Kevin Ball to discuss the different methods of age verification and their privacy implications, the long constitutional history of anonymous speech, how recent court decisions are reshaping what governments can require, the data breach and surveillance risks these mandates create, and what engineers and companies should keep in mind when they are asked to build these systems.

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TypeScript is a programming language that builds on JavaScript by adding a system of types. Those types let developers describe the shape of their data and catch mistakes before code ever runs, while also powering the autocompletion and editor tooling that many developers now rely on every day. It was first released in 2012, and has since become one of the most widely used tools in web development. TypeScript recently underwent one of the most significant changes in its history with the release of version 7.

Daniel Rosenwasser is the Principal Product Manager of TypeScript at Microsoft, where he began as an engineer on the team just weeks after the TypeScript 1.0 release. In this episode, Daniel joins Josh Goldberg to talk about the features of TypeScript 7. They discuss the TypeScript team’s approach to tooling, TypeScript’s relationship with the TC39 standards process behind JavaScript, the new API and IPC boundary, how LLMs could reshape type checking and linting, and more.

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It is widely reported that a gap has emerged between enterprise spending on AI and the durable value captured from that spend. Individual employees have enthusiastically adopted coding assistants and chatbots, yet those gains do not seem to be transforming businesses at an organizational level. One of the most important questions in the tech industry today is understanding why AI is not yet delivering returns that match the investment, and what separates the small number of enterprises succeeding from the many that are not.

Scale AI is known for supplying the human-labeled data behind many frontier models. It now also builds AI applications and agents for large enterprises. That combination of working alongside frontier labs and inside enterprise deployments gives the company a rare view of why enterprise AI may be stalling.

Emily Xue is the Head of Enterprise AI at Scale AI, and previously spent over a decade at Google. In this episode, Emily joins Kevin Ball to discuss the three layers where enterprise AI breaks down, why frontier model benchmarks miss what enterprises actually need, the data foundation problem, how the most successful companies combine internal domain expertise with outside AI specialists, and more.

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The conversation about AI often focuses on software, automation, and the race between attackers and defenders in code. However, some of the most consequential risks lie further afield, in domains where a mistake is measured in human lives. Advanced models can now offer step-by-step guidance toward chemical and biological weapons, and militaries are already folding AI into targeting and battlefield assessments. These are no longer speculative fears confined to the AI doomer crowd. They are documented in red team disclosures, government legislation, and events unfolding on real battlefields.

Gordon M. Goldstein is an adjunct senior fellow at the Council on Foreign Relations, where he focuses on the convergence of technology and US foreign policy. He previously spent nearly a decade as a managing director at Silver Lake, and he is the author of Lessons In Disaster: McGeorge Bundy and the Path to War in Vietnam, which is a study of national security strategy and White House decision-making. In this episode, Gordon joins Kevin Ball to discuss the credibility of AI-enabled chemical and biological weapon threats, the ad hoc safeguards meant to contain them, and the rise of autonomous warfare and its implications for human control and nuclear deterrence. Gordon speaks on his own behalf, not on behalf of the Council on Foreign Relations.

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News feeds and recommendation systems have long relied on deep learning architectures that score each candidate item independently. As LLMs have matured, they have opened up a fundamentally different approach, where a system can reason about content the way it reasons about language. However, that power comes with a fresh set of engineering challenges around cost, scale, and evaluation.

LinkedIn recently rebuilt its news feed to treat content recommendation as a sequence modeling problem. The general approach is to predict what a user will want next, much like an LLM predicts the next token in a sentence.

Tim Jurka has worked at LinkedIn for 13 years and is currently a VP of Engineering. In this episode, Tim joins Matt Merrill to discuss how LinkedIn re-engineered its feed, how the team combines LLMs with traditional signals, managing inference costs at massive scale, steering content quality using natural language policies, and more.

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For two decades, the cloud has been shaped by human developers writing code and managing its deployment. Now a growing share of production code is generated by LLMs with little human review. Because that code is not fully trusted, it increasingly runs in isolated, sandboxed environments. Meanwhile, AI agents are starting to operate infrastructure directly, by spinning services up and tearing them down on their own. Together these shifts raise the question of whether the cloud needs to be rebuilt for machine operators rather than humans.

Render is a cloud platform designed for application deployment, by handling scaling, self-healing, and security to reduce operations work. Render has been adapting to the AI era by building tools that let agents deploy and debug applications directly, with added guardrails and security.

Anurag Goel is the founder and CEO of Render. In this episode, Anurag joins Sean Falconer to discuss why so many teams end up rebuilding the same infrastructure on top of Kubernetes, what changes when AI agents become first-class users of infrastructure and the guardrails that shift demands, and why the economics of AI are pushing developers toward higher-level platforms that trade fine-grained control for speed and safety.

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