Agents and Engineers | Agentic AI, Software & Agentic Engineering

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 engineer, and a release manager.

The agents' operating manual is a roughly 5,000-line AGENTS.md tree spread across about 45 markdown files and served via MkDocs. The Kanban lives in GitHub Projects, milestones serve as sprints, story points and labels drive the workflow, and a "kaizen accumulator" task captures learnings each sprint that get translated into process changes at the start of the next one.

Next up, diving into token-maxxing. Ian explains why he keeps hitting Claude Max 20x weekly limits on day three of a sprint — five software engineering agents plus two QA agents burning tokens in parallel — and the management tricks he's adopted: Caveman to enforce terse prompts, templated processes, a catalog of deterministic scripts behind self-documenting skills, pre-commit hooks, and roughly a dozen CI gates that run Claude and Codex reviews against PR templates.

Still, not everything is perfect in agent-land. Ian describes his agents as "solid second quartile" engineers. They're fast, pleasant, and (currently) inexpensive, but wrong in meaningful ways on one PR in five. Vibe coding works for prototypes and small reports, but serious systems still need human-driven design thinking, separation of concerns, and testing discipline.

Perhaps the current moment is an "interregnum" between 25 years of established software practice and an agent-native future. Could this one day be a software factory with human "forepersons" running follow-the-sun shifts over agents that never sleep? The episode closes with a warning about "AI brain fry" that comes from work products arriving ten times faster than humans produce them.

Full episode notes

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Chapters

• (00:00) - Welcome and Ian's background

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Links from the show

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• PNI AI Studio

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Guests

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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 Minivac 601 circuit simulator project becomes the central cautionary example: months of fruitless AI-assisted attempts to simulate relay circuits collapsed once he realized he needed a real physics engine rather than asking the AI to re-derive Kirchhoff's laws from scratch.

A recurring theme is the tension between speed and trust. Greg describes his journey from clicking "yes" to every Claude permission prompt, to briefly trying sandboxing tools like Nono, to ultimately running Claude with dangerously-skip-permissions locally — partly out of pragmatism, partly because he concluded the permission theater wasn't actually catching anything. He shares his "committee of elders" technique, routing important decisions through Claude, Gemini, and ChatGPT simultaneously and only proceeding when all three agree. Dan shares his MMI hook tool, which intercepts Claude's bash calls to enforce conventions like always using uv instead of raw Python.

The episode closes with a candid discussion of the emotional and societal costs of this pace. Greg describes a new kind of frustration — distinct from normal debugging — when an AI tool fails after drawing you deep into a rabbit hole. He and Dan also address broader concerns: the acceleration of security vulnerabilities, the environmental cost of GPU compute, and AI-driven job displacement. Both acknowledge they can't stop using these tools even as they see the harms compounding, and end on a cautiously hopeful note about open-source and local models eventually offering more control.

Full episode notes

Click here to view the episode transcript.

Chapters

• (00:00) - Introduction and guest background

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Links from the show

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• Claude Code

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Guests

-------

Follow the podcast

-------------------

• LinkedIn

⠀

Follow Dan Gerlanc

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• X

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.