Agents and Engineers | Agentic AI, Software & Agentic Engineering

Trevin Chow is has more than 25 years of experience in product and design, held product leadership roles at Axon, Sketchy, Nike, and Microsoft, and co-founded Chewsy. He is a core maintainer of Compound Engineering, a plugin for Claude Code, Codex, and other agent harnesses.

Dan and Trevin discuss how AI changes the work of building software, especially design and product judgment. Trevin describes starting with a value proposition before generating designs, using visual references to guide exploration, and keeping humans involved in critique. His “AI sandwich” puts human attention on defining the work and polishing the result while agents handle implementation.

Concrete examples include illo's character packs for consistent illustrations and Compound Engineering's reviews across model families. Trevin explains why newer models call for clearer goals and fewer prescribed steps, while extensive evaluations, changing model behavior, and outdated plugins make maintenance costly.

The conversation also covers the supervised Hermes Agent setup Trevin built for his daughter, including boundaries around companionship and homework help. It closes with Jev-powered prototypes and Energon, his open source service for sharing documents and files across agents without losing control of the underlying context.

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Chapters

• (00:00) - Product optimism and the changing craft of software

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Links from the show

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• Compound Engineering

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Guests

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Trevin Chow, Product Leader and Builder

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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 retains the final merge decision.

Their engineering choices favor quick feedback and manageable costs. Vycari is separating microservices into their own repositories and runs Python and Postgres on VPSs. Allen explains how inexpensive models, stronger models writing their procedures, and deterministic integrations support the goal of a $5 monthly subscription.

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Chapters

• (00:00) - Allen's path to Gemini CLI

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Links from the show

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• Vycari
• Gemini CLI
• Claude Code
• Greptile
• GitHub
• Obsidian
• Antigravity SDK
• Roborev
• Hostinger
• Python
• Postgres
• XLA
• Theano
• JAX
• CUDA
• Gemini Flash Lite
• Claude Haiku
• GPT Luna
• Gemma
• GLM
• DeepSeek
• Supabase
• Neon
• Modal
• microservices
• continuous integration
• fine-tuning
• you ain't gonna need it

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Guests

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Allen Hutchison, Co-Founder & CTO, Vycari

• Website
• Vycari
• LinkedIn
• GitHub
• X

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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 explanations unreliable. The conversation ends with the human experience behind fiction and songs, and open questions about whether models could be conscious.

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Chapters

• (00:00) - Learning with LLMs and the limits of explanation

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Links from the show

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• Mistral

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Guests

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Ben Morss, Developer Advocate, Mistral

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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.

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Chapters

• (00:00) - Self-service infrastructure and the arrival of agents

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Links from the show

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• Massdriver
• Massdriver Architect
• The Citizen Developer
• Cory O'Daniel's personal website
• Terraform
• OpenTofu
• Postgres
• GraphQL
• Kubernetes
• attribute-based access control
• test-driven development
• domain-driven design
• Ghostty
• Checkov
• Wiz
• Snyk
• GitOps
• OCI
• Elixir
• Golang

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Guests

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Cory O'Daniel, CEO & Co-Founder, Massdriver

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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.

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Chapters

• (00:00) - Contributing to open source with coding agents

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Links from the show

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• Gas City

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Guests

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Stephanie Jarmak, AI Engineer, Omni

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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.

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Chapters

• (00:00) - Why Eric's data science workflow changed so quickly

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Links from the show

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• Marimo

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Guests

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Eric Ma, Senior Principal Data Scientist, Moderna

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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.

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Chapters

• (00:00) - Introduction and the gap between individual and team gains

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Links from the show

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• Beads

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Guests

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Andrew Zigler, GTM Engineer, LinearB; Host of Dev Interrupted

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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

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Chapters

• (00:00) - A closed-loop coding agent

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Links from the show

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• Skipper

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Guests

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Julien Verlaguet, Founder and CEO, SkipLabs

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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.

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Chapters

• (00:00) - Agentic engineering meets Bayesian decision science

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Links from the show

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• Master Agentic Data Science
• Show Us Your Agent Skills
• PyMC
• PyMC Labs
• Bayesian modeling
• causal questions
• media mix modeling
• Daimon
• Alchemize
• PyMC Forecast
• prior data fitted networks
• NumPyro Forecast
• Stan
• JAX
• PyTorch

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Guests

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Thomas Wiecki, Founder, PyMC Labs

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• GitHub
• Twitter

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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?

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Chapters

• (00:00) - Agents, engineers, and reverse centaurs

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Links from the show

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• Build Production-Ready AI Agents for the Enterprise

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Guests

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Hugo Bowne-Anderson, Independent Data and AI Scientist

• Website

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Doug Turnbull, Principal, SoftwareDoug

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