Agents and Engineers | Agentic AI, Software & Agentic Engineering

Dan Gerlanc

The podcast about agentic AI, agentic software engineering, and entrepreneurship. Each episode is a conversation with people building with agentic AI. Join me as I follow the stories, the behind-the-scenes, and the people behind the code. About your host, Dan Gerlanc: Dan brings his experience as a 4x founder with 20 years of experience in ML and software to find unique insights on the impact of AI in tech, software engineering, and entrepreneurship.

  1. 6d ago

    Agents, Engineers, and Citizen Developers

    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 deployment paths for domain experts who can now create software themselves, with collaboration and operational ownership still needing attention. Full episode notes Transcript Chapters (00:00) - Self-service infrastructure and the arrival of agents (03:11) - An API built around infrastructure context (09:03) - Agent identity and attribute-based permissions (15:54) - A four-person team, agents, and technical debt (23:12) - Future engineering roles and enforceable quality (30:20) - Backlog zero and autonomy for citizen developers (34:29) - Claude Code workflows, examples, and memory (40:54) - Testing infrastructure changes before production (48:00) - Reusable presets and shared provisioners (55:06) - Terraform is easy; organizations are complex (57:09) - Citizen developers and the future of cloud platforms (01:00:53) - When production apps run on someone's laptop (01:03:23) - Domain expertise, autonomy, and engineering's value (01:10:30) - Collaboration beyond Git and code in OCI registries ⠀ Links from the show -------------------- MassdriverMassdriver ArchitectThe Citizen DeveloperCory O'Daniel's personal websiteTerraformOpenTofuPostgresGraphQLKubernetesattribute-based access controltest-driven developmentdomain-driven designGhosttyCheckovWizSnykGitOpsOCIElixirGolang⠀ Guests ------- Cory O'Daniel, CEO & Co-Founder, Massdriver WebsiteLinkedIn⠀ Follow the podcast ------------------- LinkedInThreadsInstagramTikTok⠀ Follow Dan Gerlanc ------------------- XLinkedInThreadsBluesky

  2. Sep 29

    Building a Software Factory with Gas City

    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 and home. Full episode notes Transcript Chapters (00:00) - Contributing to open source with coding agents (08:51) - Learning the review standards and becoming a maintainer (12:58) - Automating a Gas City workflow (21:02) - Astronomy, computers, and a different path into development (27:40) - Working styles and responsibility for agent output (34:46) - Production responsibility and AI skepticism (40:12) - Human judgment and agents solving science tasks (45:55) - Pre-mortems, silent failures, and too many alerts (48:34) - Slack as an agent dashboard and shareable Gas City packs (53:45) - Building games and creative projects with the kids (59:06) - From planetary science to search and Sourcegraph (01:09:48) - Omni and a role shaped around the person (01:15:03) - Decision models and making automation easier ⠀ Links from the show -------------------- Gas City Beads Gas Town Dolt Steve Yegge Claude Code Codex The Six Types of Working Genius Terminal-Bench Science Harbor Slack Suno Forge Scryfall 17Lands SciX Sourcegraph Amp Omni information retrieval pre-mortem reinforcement learning from human feedback Jev ⠀ Guests ------- Stephanie Jarmak, AI Engineer, Omni Website LinkedIn ⠀ Follow the podcast ------------------- LinkedIn Threads Instagram TikTok ⠀ Follow Dan Gerlanc ------------------- X LinkedIn Threads Bluesky

  3. Sep 22

    Agentic Data Science in a Reproducible Marimo Notebook

    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 trap. Full episode notes Transcript Chapters (00:00) - Why Eric's data science workflow changed so quickly (05:49) - Marimo Pair puts agents inside the notebook (07:19) - Reproducing a protein-engineering paper (10:45) - Why enzyme stereoselectivity matters (13:20) - Canonical data sources and portable environments (18:08) - Exploring activity and selectivity with interactive plots (27:47) - Finding promising regions for protein engineering (30:24) - From heat maps to a 3D protein viewer (35:54) - Learning from agent traces and preserving intent (47:13) - Opinionated structure for agent-written code (52:25) - Managing attention across agentic tasks ⠀ Links from the show -------------------- Marimo Marimo Pair OpenCode cmux Machine-Directed Evolution of an Imine Reductase for Activity and Stereoselectivity cloudscraper Polars Plotly PEP 723 uv 3Dmol.js anywidget Protein Data Bank PyMOL The Arrow of Intent Hermes Agent Obsidian ⠀ Guests ------- Eric Ma, Senior Principal Data Scientist, Moderna Website LinkedIn GitHub X ⠀ Follow the podcast ------------------- LinkedIn Threads Instagram TikTok ⠀ Follow Dan Gerlanc ------------------- X LinkedIn Threads Bluesky

  4. Sep 15

    Beads, Better Specs, and Less Rework

    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 built on simple primitives (17:37) - Mise en Place and planning before coding (21:58) - Connecting Beads to team workflows (26:10) - Why task graphs help agents maintain context (35:57) - Shared specs and human alignment (43:26) - Agile iteration with faster prototypes (44:52) - CI/CD and independent code review (52:12) - Humanities, learning, and managing agents (58:00) - Changing skills and software interfaces (01:02:25) - Fixing feedback loops and sharing the stack ⠀ Links from the show -------------------- Beads Beads Rust Mise en Place Andrew’s dotfiles Dev Interrupted LinearB Tailscale Agent Gateway systemd WireGuard DuckDB Wispr Flow Dolt Gas Town Steve Yegge Jeffrey Emanuel Robots Ate My Homework NumPy pandas The Diamond Age Snow Crash agile continuous integration progressive web app ⠀ Guests ------- Andrew Zigler, GTM Engineer, LinearB; Host of Dev Interrupted Website LinkedIn X GitHub ⠀ Follow the podcast ------------------- LinkedIn Threads Instagram TikTok ⠀ Follow Dan Gerlanc ------------------- X LinkedIn Threads Bluesky

  5. Sep 8

    Programming Languages for AI Agents

    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 to six agents (41:04) - Why code quality still needs judgment (49:48) - The hidden cost of AI-generated tests (56:24) - The junior engineer dilemma (01:04:27) - Formal methods and LLM verification (01:13:35) - A bumpy road toward a brighter future ⠀ Links from the show -------------------- Skipper SkipLabs Skip Hack reactive programming incremental computation Buck SQLite Coq Rocq Lean Curry-Howard correspondence formal methods model checking Emacs Bun ⠀ Guests ------- Julien Verlaguet, Founder and CEO, SkipLabs Website LinkedIn ⠀ Follow the podcast ------------------- LinkedIn Threads Instagram TikTok ⠀ Follow Dan Gerlanc ------------------- X LinkedIn Threads Bluesky

  6. Sep 1

    AI Agents for Bayesian Data Science

    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 like Slack or Discord to democratize access to company information, and what parts of data science still require talking with humans to understand the problem. Full episode notes Transcript Chapters (00:00) - Agentic engineering meets Bayesian decision science (05:27) - Bayesian models versus black-box prediction (10:22) - Confounders and causal mechanisms (12:41) - From notebooks to model-aware agents (15:41) - The rise of agentic interfaces (19:18) - Alchemize and verified code translation (29:49) - Agentic data science beyond the silo (34:25) - Daimon and collaborative multiplayer AI (37:43) - Verification and unsettled methods (42:53) - Parallel agents and PyMC Forecast (51:08) - The Bayesian method of software engineering (53:59) - Agents as software's primary users (57:08) - Institutional knowledge and bus-factor risk (01:03:56) - Faster research and open source (01:12:05) - Causal models for AI's future ⠀ Links from the show -------------------- Master Agentic Data ScienceShow Us Your Agent SkillsPyMCPyMC LabsBayesian modelingcausal questionsmedia mix modelingDaimonAlchemizePyMC Forecastprior data fitted networksNumPyro ForecastStanJAXPyTorch⠀ Guests ------- Thomas Wiecki, Founder, PyMC Labs WebsiteLinkedInGitHubTwitter⠀ Follow the podcast ------------------- LinkedInThreadsInstagramTikTok⠀ Follow Dan Gerlanc ------------------- XLinkedInThreadsBluesky

  7. Aug 25

    Search is Eating AI

    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 search code. As to other code, Hugo describes the "dark factory" pattern, where code is written by AI agents but cannot be directly read by humans. How do we design systems for a world where the primary consumers of code are AI agents, not humans? Full episode notes Transcript Chapters (00:00) - Agents, engineers, and reverse centaurs (01:24) - Three paths for agentic search (05:32) - Teaching production-ready enterprise agents (07:06) - Why search is eating AI and data (10:24) - Start with evals and retrieval baselines (16:08) - Writing code to build judgment (18:45) - Learning and building beyond code (24:39) - Verification in a world of abundant code (29:52) - Resisting agent-driven feature sprawl (33:38) - Specialized models and better agent interfaces ⠀ Links from the show -------------------- Build Production-Ready AI Agents for the Enterprise Vanishing Gradients retrieval-augmented generation BM25 NDCG Terence Tao dark software factories lights-out manufacturing Unix philosophy OpenClaw Simon Willison ⠀ Guests ------- Hugo Bowne-Anderson, Independent Data and AI Scientist Website LinkedIn X GitHub Bluesky ⠀ Doug Turnbull, Principal, SoftwareDoug Website LinkedIn X GitHub Bluesky ⠀ Follow the podcast ------------------- LinkedIn Threads Instagram TikTok ⠀ Follow Dan Gerlanc ------------------- X LinkedIn Threads Bluesky

  8. Aug 18

    The Handoff Packet for Local Agentic AI

    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 developers retain to read, test, and own the code they ship. Full episode notes Transcript Chapters (00:00) - A profession changing by the hour (02:54) - Why Python still matters with humans in the loop (04:37) - Static analysis for small local models (11:51) - DwarfStar and the local inference stack (17:28) - Apple's on-device AI push (23:29) - Why unsupervised agent runs fall apart (28:46) - The big brain, medium brain, little brain (41:19) - Spec drift and Markdown debt (47:45) - Pyrefly and duck typing for agents (51:22) - Better tool calling for local coding models (01:00:10) - Community-maintained Python training data (01:03:42) - Bring back the engineering ⠀ Links from the show -------------------- arXivPydantic AILM StudioOpenRouterDeepLearning.AI spec-driven development courseSpec KitSuperpowersPyreflyMellumFlowerLiquid AI⠀ Guests ------- Paul Everitt, Python and Web Developer Advocate at JetBrains WebsiteLinkedInDeepLearning.AI: Spec-Driven Development with Coding Agents⠀ Follow the podcast ------------------- LinkedInThreadsInstagramTikTok⠀ Follow Dan Gerlanc ------------------- XLinkedInThreadsBluesky

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About

The podcast about agentic AI, agentic software engineering, and entrepreneurship. Each episode is a conversation with people building with agentic AI. Join me as I follow the stories, the behind-the-scenes, and the people behind the code. About your host, Dan Gerlanc: Dan brings his experience as a 4x founder with 20 years of experience in ML and software to find unique insights on the impact of AI in tech, software engineering, and entrepreneurship.

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