Agents and Engineers | Agentic AI, Software & Agentic Engineering

Allen Hutchison is the co-founder and CTO of Vycari, building AI agents for everyday tasks with Chris Perry. He spent 21 years at Google, most recently as a Distinguished Engineer at Google DeepMind, helped found Gemini CLI, and built the Antigravity SDK. His earlier leadership work included Google's mapping products, Core ML, and the AI Developer organization. He writes Letters from Silicon Valley. Dan and Allen discuss why consumer agents need familiar interfaces. Vycari embeds agents in mini apps, including a calendar booking tool that works with meeting preferences and buffer time. Allen wants users to think about the assistant's usefulness rather than the technology behind it. Vycari's two founders have landed well over a thousand PRs in roughly a month. Planning documents guide implementation, nightly jobs keep those documents aligned with code, and review follow-ups become issues that agents can implement. Independent reviews and reflection on user trajectories help identify problems, while Allen…

Ben Morss is a Developer Advocate at Mistral. He previously spent 9.5 years at Google as a Product Manager for advanced APIs in Chrome and a Developer Advocate for the web. He earned a bachelor's degree in Computer Science and Music from Harvard University and a doctorate in Music Composition from the University of California at Davis. He continues to make music and write about music theory. Dan and Ben discuss how LLMs help people learn while still missing context and intent. Their discussion of information theory and training data leads into a debate about software craft when agents make code much cheaper to produce. Ben describes Chat Invaders, a cat-themed demo game, and a reunion sing-along app that broke with forty people present. He had not load-tested the app and did not determine what caused the failure. They explore why working output does not guarantee value for a business or an audience. Ben wants musical assistance that can develop ideas and handle arranging work, but finds music theory…

Cory O'Daniel is CEO and co-founder of Massdriver, an internal developer platform and platform orchestrator for governed self-service infrastructure using Terraform, OpenTofu, and Helm. He has spent more than 20 years building teams and startups and working with cloud infrastructure. He created Bonny, an Elixir-based Kubernetes operator framework, and is a co-founder of OpenTofu. Dan and Cory discuss what infrastructure agents need to act safely. Massdriver models environments and resource relationships so agents can query context directly, while separate agent identities and attribute-based permissions constrain their access. Cory describes a staging workflow that tests infrastructure changes through real deployments before he promotes a release to production. Cory explains how a tested, consistent codebase supports his team's Claude Code workflow, why enforceable tooling matters more than long instruction files, and why human review still matters. He argues that engineers should build quality controls and…

Stephanie Jarmak is an AI Engineer at Omni working on multi-agent orchestration and code intelligence. She is a research affiliate for NASA's Science Explorer (SciX) and a maintainer of Gas City, an open-source orchestration builder for multi-agent coding workflows. Dan and Stephanie discuss how she became a Gas City maintainer by learning from review agents, encoding contribution standards in skills, and building an issue-triage workflow. Automating those steps exposed new reliability problems, from failures across dependencies to excessive alerts and agents that overreact to hypothetical risks. Their conversation connects these experiments to Stephanie's move from planetary science into search and AI engineering. They examine the appeal of building with agents, the responsibility for maintaining what those agents produce, and the mixed emotions of seeing models solve once-difficult research tasks. Personal game projects and Omni's approach to shaping her role show how her curiosity carries across both work…

Eric Ma is a Senior Principal Data Scientist at Moderna, where he leads the Data Science and Artificial Intelligence (Research) team. Previously, he conducted biomedical data science research at the Novartis Institutes for Biomedical Research and earned his doctorate in Biological Engineering from MIT. Eric is also an open-source software developer known for leading pyjanitor and nxviz and contributing to NetworkX and PyMC. Dan and Eric discuss Eric's rapidly changing practice of agentic data science, centered on Marimo Pair and reproducible Python notebooks. Eric demonstrates an analysis of protein activity and stereoselectivity data, explains why canonical sources and inline environment metadata matter, and shows how agents make ambitious custom visualizations more approachable even when a live demo fails. The conversation then turns to design documents, testable specifications, repository conventions, and the attention-management practices Eric uses to keep agent work from becoming a context-switching…

Andrew Zigler is a GTM Engineer at LinearB and the host of Dev Interrupted, a twice-weekly podcast and newsletter about AI-native development and agentic orchestration. A classicist by training, he previously taught in Japan, built e-learning platforms, and worked in developer relations at Mattermost. Dan and Andrew discuss how shared workflows and memory can help turn individual AI gains into team improvements. Andrew explains his Mise en Place planning methodology and how he uses Beads to translate ideas into connected tasks, while keeping human collaboration in tools such as Asana and Confluence. They examine why clear specs reduce rework without removing the need for iteration, how CI/CD and code review may adapt to agentic development, and what Andrew’s personal agent setup has changed about his work and learning. Full episode notes Transcript Chapters (00:00) - Introduction and the gap between individual and team gains (06:15) - Open source when agents become the users (10:09) - A personal agent stack…

Julien Verlaguet is Founder and CEO of SkipLabs, the company building Skipper, a closed-loop coding agent. He created Skip, a reactive programming language, and led the design of Hack, the language Meta developed to run its code base at scale. In this episode, we discuss what the job of programming language designers becomes when AI agents write most of the code. This includes the design decisions in the language and tooling when agents are the principal user. We also discuss: - How Julien refocused SkipLabs towards AI - What it would take for AI to be able to fully replace engineers - The use of formal methods with LLMs Full episode notes Transcript Chapters (00:00) - A closed-loop coding agent (04:30) - Reactive programming and technical debt (08:40) - Why SkipLabs moved toward AI tooling (11:07) - Incremental tools and low-latency feedback (14:20) - Reactive collections versus Buck and Make (21:06) - Language design in the age of LLMs (31:01) - When human coding still matters (34:16) - Working with four…

Thomas Wiecki, Founder, PyMC Labs, is the author of PyMC, one of the most popular frameworks for Bayesian modeling, and the founder of PyMC Labs, a Bayesian AI consultancy. He holds a PhD in Computational Cognitive Neuroscience from Brown University and previously served as VP of Data Science and Head of Research at Quantopian Inc., where he built and led a team of data scientists to develop a hedge fund leveraging insights from a crowd-sourced pool of 300,000 researchers. In this episode, we explore how AI agents have made Bayesian methods more accessible to non-experts. Bayesian models are useful for both forecasting and decision-making under uncertainty. These models can act as scenario simulators, making it possible to ask what-if questions about marketing, seasonality, or health interventions while exposing the mechanisms behind an outcome. We also discuss how agentic engineering has finally helped deliver on some of the original promises of data science, how to effectively embedding agents in tools…

Dan is joined by Hugo Bowne-Anderson and Doug Turnbull. Hugo is an independent data and AI scientist who has advised and taught teams at Netflix, Meta, and Amazon. Doug is an independent consultant and search expert with experience at Shopify, Reddit, and Wikipedia, and the author of "Relevant Search" and "AI Powered Search." In this episode, they discuss what agentic search actually means and the 3 primary ways to implement agentic search. Hugo argues that search is becoming a core skill because agents can handle questions that require repeated retrieval and synthesis. To improve agentic search, most teams should start with evals. Establish a retrieval baseline, find areas for improvement, and iterate. Defining a “good” retrieval baseline is product-specific as a healthcare assistant may need its first result to be correct, while an e-commerce system may succeed by offering several useful options. Doug and Hugo argue that the lack of good training examples means its often useful to read and write your own…

Paul Everitt is a Python and Web Developer Advocate at JetBrains. He previously co-founded Zope Corporation, helped bootstrap the Python Software Foundation and Plone Foundation, and served as a US Navy officer who started www.navy.mil in 1993. Paul and Dan discuss how local agentic AI could reshape software engineering, from smaller models supported by static analysis and better tool calling to workflows built around specs, tests, and focused handoff packets. They compare local, cloud, and hybrid model setups and examine what happens when agents work with less supervision. The episode's titular handoff packet sits at the center of a three-level workflow. A large model understands the project, a medium model creates an execution plan, and a small local model carries out a tightly scoped task. The packet gives that smaller model the context, constraints, tools, and success criteria it needs. They also discuss spec drift, Markdown debt, community-maintained Python training data, and the responsibility…

Dan and Niels Bantilan discuss how AI agents are changing Niels's work on two open-source projects, Flyte and Pandera. Flyte began as an MLOps orchestrator and is evolving into an AI runtime for the code, compute, and execution systems around models and agents. Pandera remains a smaller, community-focused data-validation project. Niels finds agents most useful in mature codebases with strong structure, linters, type checks, and tests. He estimates that his coding velocity has increased at least threefold. Local models handle small fixes, while commercial tools perform better on longer tasks that require broad codebase analysis. Pull requests and code review remain central, with reviewers checking for code smells, security problems, and performance issues. Agents now participate in Niels's debugging loop inside live Kubernetes clusters. Through Flyte's MCP server, an agent can inspect logs, identify an out-of-memory error, update the Flyte configuration, and retry the workload. In one case, an agent found an…

Dan and Jacob Young discuss what technical due diligence looks like when software teams use coding agents. Best practices have not settled, so Jacob looks less for a particular model or tool than for convergence: whether a team and its agents keep moving toward the same grounded idea of what the software should be. Shared standards, existing abstractions, language servers, linters, hooks, tests, and code review all help, but the most useful constraints arrive during development rather than after a huge pull request. The conversation turns to documentation, where Jacob sees a recurring failure mode. Teams create architecture and API documents, then let them drift out of date within weeks. Documentation helps only when the code remains the source of truth and some system can regenerate or update the documents when the code changes. He also argues that security checks belong inside the development and review workflow. Agents can apply codified OWASP practices, but they cannot be trusted to choose cryptographic…

Dan and John Berryman discuss why today’s terminal- and IDE-centered agent harnesses are too narrow. John argues that agents should be able to see and act across the applications, websites, files, and physical environments that make up a person’s life. His Rook project is an attempt to make those contexts addressable while allowing people to keep using the agent harnesses they already trust. The conversation turns to a future in which websites expose agent-facing capabilities and applications reshape themselves around conversations. John imagines an assistant that can combine a person’s notes and shopping list with location, store inventory, and aisle information. He says feasibility is the major barrier that recently moved into reach, while security, transparency, trust, and standardization remain unresolved. That tension becomes concrete in the discussion of permissions and sandboxing. John expects people to begin with constrained, read-only access and gradually grant more authority, with dry runs,…

Sheamus McGovern founded ODSC roughly twelve years ago and now splits his time between the conference business and a role as venture partner and Head of AI at Cortical Ventures. His book, The AI Skill Flip, came out of a pattern he kept hitting: data scientists and software engineers coming to him asking whether AI was going to take their jobs and what they should do about it. He wanted to write something that sat between the doom narrative and the utopian one, both of which he thinks are wrong. The "flip" in the title is the observation that the balance of skills has shifted rather than disappeared. Five years ago a software engineer spent most of their time writing raw code. Now much of that time goes to judging and evaluating what the model produced and thinking further up the stack. The same flip applies in marketing, where the skill becomes knowing what good looks like and what persona you're targeting rather than producing the asset yourself. Asked what separates people who get real value from AI from…

Matthew Rocklin is an open-source software developer best known for creating Dask, a Python library for scalable parallel and distributed computing. He has contributed to projects including Toolz, SymPy, and Theano, worked at Anaconda and NVIDIA on the RAPIDS ecosystem, and founded Coiled, a company focused on running Dask in the cloud. He has a PhD in computer science from the University of Chicago. Dan and Matthew discuss how Coiled changed after Matthew stepped away from its ambitious, VC-backed growth path. The company went from roughly fifty people at its peak to three part-time engineers, while making more money and operating more smoothly. Matthew now connects agents to Slack, email, QuickBooks, the bank, the calendar, the codebase, and company context, which lets them spot problems that cross disciplines. Matthew describes a lightweight operating system for the company. The structure is simple, a Git repository of Markdown files records customers, employees, systems, and his own context. Agents…

Dan and Jonathan Bown open with the talk Jonathan gave at ODSC, "Practical Agent Ops: From POC to Prod with MLflow 3.0." MLflow 3.0 arrived last summer as the first stable release built for generative AI rather than traditional machine learning, and Jonathan's team used it to build an agent for pre-enrollment students. The centerpiece of that work was evaluation-driven development. Instead of jumping straight into a working prototype they aligning the business up front on what quality actually looks like before signing off on a model with inherently non-deterministic output. The initial key to success was an Excel file. In it, the data science team had already assembled 150 ground truth examples, but left them untested and set aside while engineers focused on code. Jonathan's team paused the coding work and ran a simple foundation model against those examples first, landing at what amounted to a coin flip of useful versus hallucinated answers. From there they refined the examples with the business, loaded…

Dan and Greg open with how agentic development has changed since the early days of Copilot. At the time, Greg was at GitHub, and he saw AI mostly help with boilerplate and editor completions. Cursor-style agents were the next widely-used advancement bringing session history and integrated team-wide practices. By June 2026, capable models and harnesses are common inside engineering teams, so the gap between teams increasingly comes from context engineering, repository structure, and whether old team shapes still align with the new ways of building software. For small teams and startups, the leverage of AI is a double-edged sword. Greg describes how SpecStory's original extensions required real sweat equity to reverse engineer chat-log formats across Cursor, Copilot, Claude Code, Amp, and other tools. Now, much of that surface can now be maintained by a fraction of one person's time. The danger is that easy MVPs can trick founders into believing they have validated a market. When the marginal cost of software…

Dan and Eleanor open by discussing how fast software engineering has changed. In the last six months, Eleanor's practice flipped from treating AI as a messy assistant that needs close supervision to building systems that put the agents on the path to success. She now writes essentially no code herself, arguing that the models have become good enough that her involvement mostly makes the results worse. This journey starts from babysitting agents locally to delegating to async, cloud-based agents like GitHub Copilot, Cursor, Devin, OpenHands, or Factory. Eleanor warns that the home-grown terminal "loops" everyone is building right now are great for learning but too brittle to scale. Next up, what does an agent engineering system actually need? Eleanor recommends starting with a sandboxed, execution environment (usually containers), careful configuration over how the agent reaches the outside world (MCP servers and selective network access), a way to see across multiple repositories, and layered rules via…

Dan, Angie Jones, and Demetrios Brinkman open with a discussion of the Agentic AI Foundation ("AAIF"), founded by Anthropic, OpenAI, and Block in December 2025 and now home to roughly 180 member companies. AAIF recently launched an ambassador program (apply [here](https://aaif.io/ambassadors/)) and has upcoming events across the globe from [AGNTCon](https://events.linuxfoundation.org/agntcon-mcpcon-north-america/) in San Jose to gatherings in Amsterdam, India, Tokyo, and Seoul. A recurring theme is that the whole industry is learning agentic engineering together. So get out of your "lab" and compare notes! You don't have to do all of this R&D on your own (well, maybe some of it, but it doesn't hurt to collaborate). Everything is changing. And quickly. Angie marks the release of Claude Opus 4.5 as when agentic engineering became viable. Where engineers once obsessed over context engineering and priming a repo so an agent had a chance, the latest frontier models often just need to be pointed at a codebase and…

Dan and Ian Stokes-Rees, founder and CEO of PNI AI Studio, open by discussing the thesis of Ian's company: an opinionated stack of open-source tools wrapped in agentic AI so business analysts, managers, and finance teams can get the capabilities of a senior data scientist without learning Python, SQL, or R. Ian's primary target is financial services, where an estimated 200+ million weekly Excel users still run human-driven, tacit-knowledge processes. He frames a second opportunity, capturing the "AI exhaust" coming out of those workflows, as the seed for a follow-on product. The conversation turns to how Ian actually builds his product. Ian walks through a three-phase evolution: Cursor as a coding assistant, prompt-based Claude Code generation, and finally a full agentic team modeled on Steve Yegge's "Gas Town" post. Today he runs six to ten Claude Code agents in named roles. Xavier and Yasmin are Agile Process Managers, Anne is the Principal Architect. Now add in software engineers, QA engineers, a test…

Dan and Greg discuss Revise Robotics, where Greg serves as founding engineer building robotic systems that refurbish discarded corporate laptops for donation. The episode opens with a description of how AI vision models allow robots to navigate unfamiliar BIOS screens and unpredictable laptop states dynamically — a capability that wasn't feasible a few years ago. Greg reflects on how LLM-powered vision surprised even him as a "second gift," enabling a kind of general adaptability that previously would have required exhaustively pre-coded state machines. The conversation digs into Greg's hands-on experience using Claude for hardware projects, most vividly illustrated by an Arduino RPC library he built on a Raspberry Pi in under five minutes — a task he estimates would have taken a full day by hand. Greg draws a sharp distinction between projects where AI delivers near-100x speedup (well-defined problems with existing patterns and a testable harness) versus cases where it gets confidently stuck in loops. His…

Welcome to Agents and Engineers: The podcast about Agentic AI and Software Engineering. I'm your host, Dan Gerlanc. Each episode is a conversation with people whose daily lives most intersect with AI and agentic systems. These are the people building the agents we interact with every day, like Claude and Codex. They are the engineers building software in a radically new way, by written spec and probabilistic iteration, repeated until the program works. Beyond the technical deep dives, we explore what AI and Agents mean for the future of software engineering. We discuss the psychological and philosophical implications of our interactions with this technology. Join me as I follow the stories, the behind-the-scenes, and the real people behind the code, the agents and the engineers.