『Agents and Engineers』のカバーアート

Agents and Engineers

Agents and Engineers

著者： Dan Gerlanc
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The podcast about Agentic AI and Software Engineering. Each episode is a conversation with people whose daily lives most intersect with AI and agentic systems. Join me as I follow the stories, the behind-the-scenes, and the real people behind the code.© 2026 Dan Gerlanc LLC 経済学
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  • When Software Gets Cheap, Focus Gets Expensive

    When Software Gets Cheap, Focus Gets Expensive
    2026/07/02
    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 falls, founders have to spend more of their scarce attention on demand, willingness to pay, distribution, and the routes to customers. The conversation turns to Greg's book, 25 Patterns in Agentic Engineering. He explains how he mined roughly 1,300 preserved SpecStory sessions and nearly 5,000 commits to extract durable patterns from his own agentic practice. Two patterns stand out. First, when code becomes free, verification becomes the bottleneck. Second, between agents turns, docs are the persistent API of the system. For Greg, as-built architecture documents are practical maps that let both humans and agents recover the shape of a subsystem without re-reading the entire codebase every time. Greg's development practice has changed accordingly. He favors trunk-based development and says his team uses almost no pull requests for everyday development, partly because agent-generated diffs arrive at a volume he does not want to review line by line. He prefers local agents over cloud agents that containerize the repo and open PRs later, because steering an agent while it runs keeps his mental model intact. Long unattended runs still make sense to him, but only when they start from a clear goal and a more detailed rider, with phased commits and verification points he can inspect after a walk or a night away. Dan and Greg also dig into coordination at larger scale. Greg is skeptical that issue trackers were ever clean or current enough to describe day-to-day engineering, but he sees issues becoming useful as specs with provenance and evidence that can be handed to agents. Personally, he runs several projects at once, usually three to five, with local agents in permissive modes, and rotates attention while long runs execute. That power is not free. He describes the dopamine loop of watching ideas come to life, the temptation to keep agents busy overnight, and the scarcity mindset created by subsidized access to frontier models. The episode closes with where Greg still does not trust the tools. Copywriting and visual design still require heavy human intervention because the models can blur rather than sharpen the message. He frames taste less as a mystical trait and more a selection amongst trade-offs and the ability to connect ideas in understandable ways. Coding has benefited from benchmarks and verifiable answers; much of the rest of the world is less tractable because there is no single ground truth for what "good" means. Full episode notes Click here to view the episode transcript. Chapters (00:00) - Introduction and guest background(00:55) - What agentic teams are running into(06:56) - Startup leverage, MVP traps, and maintaining SpecStory(09:26) - When software gets cheaper, distribution matters more(12:31) - Hand-written code, craft, and code as liability(16:21) - Mining 1,300 sessions into 25 patterns(19:01) - Verification and as-built architecture docs(23:55) - Co-writing docs with LLMs(25:15) - Keeping docs fresh through skills, Git, and verbose commits(27:50) - Trunk-based development for agentic teams(30:26) - Local steering versus cloud-agent pull requests(32:14) - Goal and rider plans, long runs, and Gas Town(35:52) - Replacing issue trackers with weekly docs(38:19) - Larger teams and issues as agent-ready specs(42:45) - Parallel projects and concentration limits(44:47) - Local agents, permissions, and risk judgment(46:57) - The cognitive pull of managing agents(51:58) - Scarcity, token costs, and model choice(58:57) - Copy, design, naming, and taste(01:05:04) - Why creative output resists verification(01:07:12) - Closing ⠀ Links from the show -------------------- Hardcore Agentic Engineering for builders who shipSpecStoryStoa25 Patterns in Agentic EngineeringAI Essentials for Tech ExecutivesMeditations on TechBeyond Code-CentricGoal EngineeringWebRTCCRDTTrunk-based developmentSteve Yegge's Gas TownDead ReckonDevinDORABearDeepSeekQwenYann LeCun ⠀ Guests ------- Greg Ceccarelli, ...
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    1 時間 8 分
  • From Supervising AI to Building Systems for It

    From Supervising AI to Building Systems for It
    2026/06/18
    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 AGENTS.md and skills. Eleanor makes the case that async delegation is a forcing function for better specifications. Deterministic feedback like static analysis and test suites are the single biggest factor in work quality because "you can't control AI with AI." She has moved to fully test-driven development and notes that current-generation models no longer find unintended workarounds to tests (e.g., deleting them) the way Claude 4 and early GPT-5 once did. Dan and Eleanor turn to adoption and skills, including how to get better at using AI with deliberate practice. Eleanor explains why she moved using Python, which she was most familiar with from use over her career, to statically typed languages like TypeScript and Go for agent work, why supply chain risk at her healthcare company has her questioning every dependency, and why she dislikes the term "junior developer." Curiosity and systems thinking, not tenure, are what matter now. The episode closes on verification and scale. Eleanor distrusts any output she can't verify, doesn't miss hand-writing code, and argues that inventing new ways to verify, including more formal methods, is the real bottleneck now that models are cheap and strong. On team size, she pushes back on the "small teams" consensus, pointing to the success of large open-source communities. Eleanor remarks that software development has become a sub-branch of systems engineering, and anyone not practicing this now will be shocked in a matter of months. Full episode notes Click here to view the episode transcript. Chapters (00:00) - Introduction(00:58) - The flip: from supervising AI to getting out of its way(03:14) - Cloud-based agents vs. rolling your own(06:08) - The primitives every agent system needs(07:43) - Why async delegation beats local babysitting(11:02) - Writing specs: Codex, Repo Prompt, and markdown(12:21) - Guardrails: AGENTS.md, skills, and deterministic checks(14:19) - Going fully test-driven(17:19) - How engineers really adopt (and hide) AI(19:38) - Getting better through deliberate practice(21:12) - From experiment to reusable skill to library(24:26) - Choosing a language: Python, TypeScript, Go(26:56) - Supply chain risk and distributing specs(29:06) - Beyond 'junior': curiosity over tenure(31:03) - Systems thinking as the durable skill(38:02) - Where Eleanor still doesn't trust AI(39:40) - Not missing the keyboard(42:43) - Keeping up with a fast-moving field(44:50) - What teaching reveals(48:27) - Verification as the real bottleneck(50:41) - Team size and open source at scale(55:48) - Closing: take agents seriously Links from the show -------------------- GitHub Copilot coding agentDevinOpenHandsFactoryCodexRepo PromptAGENTS.mdModel Context Protocol (MCP)Anthropic 'when AI builds itself'LovableVercelFormal verificationUMLJimini Health Guests ------- Eleanor Berger, Member of the Technical Staff, Jimini Health WebsiteLinkedInX Follow the podcast ------------------- LinkedInThreadsInstagramTikTok Follow Dan Gerlanc ------------------- XLinkedInThreadsBluesky
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    1 時間
  • Are We All Managers Now?

    Are We All Managers Now?
    2026/06/04
    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 told the problem. Drawing on her time leading agentic AI at Block, Angie describes the agent they build that can hold a world model across 25,000 codebases. They paired this agent with cloud workstations where an agent picks up a Jira ticket, clones the repo, and opens a PR without anyone babysitting a terminal. With this kind of firepower comes new problems that look less like coding and more like management. Demetrios argues the unglamorous topic of governance — keeping teams aligned, codifying security practices, deciding what belongs in "the harness" — are the new challenges companies are grappling with. Sandboxes and cloud workers have gone mainstream. The group pushes back on the wave of AI-justified layoffs, worrying that companies are cutting the very mentorship and middle-layer "glue" needed to steer agents. They also dig into tokenomics: budgets blown by mid-year, tools that can cost more than the engineer using them, and Angie's hard-won lesson at Block that getting 95% of engineers onto coding agents produced no velocity until she funded a small group of "AI champions" to learn the tools properly. Tokens, everyone agrees, are not the same as value. As to what the group has found effective for agentic engineering, Angie makes the case for RPI (Research, Plan, Implement) from HumanLayer and for adversarial review. A 32-file refactor that earned a clean pass from Codex made her a believer. Alongside review skills, the [Council of Mine MCP server](https://github.com/block/mcp-council-of-mine), and Jesse Vincent's [Superpowers](https://github.com/obra/superpowers) skill pack; Dan adds Wes McKinney's [RoboRev](https://github.com/wesm/roborev) for continuous background review. The episode closes on the human side: whether "we're all managers now," the identity crisis facing engineers who loved the craft, how Angie found the same flow state building agents that she once found writing code, and how all of this democratizes building for non-engineers. A few quick stops to discuss the token-saving Caveman skill, naming your agents, and a duck-themed calendar app. There's still no free lunch, Dan notes, but the price has come down. At least until the next model drops. Full episode notes Click here to view the episode transcript. Chapters (00:00) - Welcome and introductions(01:49) - Inside the Agentic AI Foundation and the ambassador program(03:38) - A global slate of events and meetups(07:13) - What engineers are doing differently than six months ago(10:16) - Agentic engineering at enterprise scale and cloud workers(12:32) - Governance, the harness, and sandboxes(15:10) - Do we still need managers and the human 'glue'?(21:14) - The bill comes due: AI tool budgets(23:39) - Tokens aren't velocity and the 'AI champions' experiment(28:25) - Front-loading design versus vibe coding(30:15) - RPI and Codex as co-reviewer(34:15) - Adversarial review, Council of Mine, and Superpowers(39:34) - Robo Rev and the QA-agent pattern(42:58) - Agents, data analysis, and specifying the problem(46:33) - Are we all managers now?(48:00) - The Caveman skill and the limits of saving tokens(51:49) - Naming agents, Codex pets, and Quakpit(56:16) - Managing agents versus the joy of writing code(01:02:07) - Democratizing building and the falling price of software Links from the show -------------------- Agentic AI FoundationRPI (Research, Plan, Implement)SuperpowersroborevCouncil of MineCavemancmuxcontext rotLLM Council (Andrej Karpathy)MLOps CommunityDavis TreybigQuakpitDaskFlying Toasters (After Dark)Broomy Guests ------- Angie Jones, VP, Agentic AI Foundation LinkedIn Demetrios Brinkman, Founder, MLOps Community WebsiteLinkedIn Follow the podcast ------------------- LinkedInThreadsInstagramTikTok Follow Dan Gerlanc ------------------- XLinkedInThreadsBluesky
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    1 時間 5 分
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