The AI Native Dev - from Copilot today to AI Native Software Development tomorrow

AI coding agents now write almost all of Legora's code, with engineers running up to ten in parallel. Yet the CTO behind that shift thinks measuring adoption by tokens burned is a mistake. Jacob Lauritzen, CTO of Legora, joins Simon Maple to unpack what actually scales when agents do the typing, and where human judgment still has to stay in charge.

What we cover:
– How Legora's engineers orchestrate many AI coding agents at once, from shared local dev resources to cloud agent setups
– An agentic loop that reproduces, fixes and tests bugs straight from Slack
– Why token maxing is a bad way to drive AI adoption, and what to measure instead
– Agent evaluation: regression testing the harness as models and prompts change every week
– Decision models, the verifier's rule, and where autonomy ends and human judgment begins
– Why AI agents need more than a chat interface

Chapters:
00:00:00 - Introduction
00:01:47 - Legora's growth from $100M to $200M ARR
00:03:44 - Parallel coding agents and bug-fix loops
00:07:15 - Senior engineers vs AI-first engineers
00:10:10 - Why token maxing is the new lines of code
00:14:13 - What AI can and can't verify in legal work
00:17:30 - Decision models vs autoregressive LLMs
00:24:22 - Weekly model benchmarks and harness evals
00:26:58 - Why AI agents need more than a chat interface
00:35:03 - The verifier's rule and litigation strategy

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Is token count telling you anything real about how your team uses AI coding agents? Tell us in the comments.

Honeycomb went from 30 to 70 merged pull requests a day in three months. The catch: automated code review, not code generation, became the real bottleneck of software automation. Liz Fong-Jones, Technical Fellow at Honeycomb, explains why incidents still rose 1.5x, how their internal bot Autobot now reviews every PR, and why AI amplifies whatever org you already have.

What we cover:
– How automated code review lets humans focus on design, not trivial bugs
– Using a decision model like Jev to decide which PRs are safe to auto-merge
– What makes a codebase ready for AI coding agents
– When to trust AI agents with production incidents, and when they're just throwing darts
– Why "Claude did it" isn't an excuse, and what ownership means with AI
– How open source maintainers can handle a flood of AI slop pull requests

Chapters:
00:00:00 - Introduction
00:06:41 - Why AI amplifies dysfunctional engineering orgs
00:10:45 - What makes a codebase AI ready
00:14:01 - Honeycomb's Autobot and automated code review
00:19:06 - Using Jev to decide which PRs are safe to merge
00:26:51 - Trusting AI agents during production incidents
00:30:40 - Least privilege and guardrails for coding agents
00:33:25 - If your name's on it, you own it
00:38:55 - AI slop pull requests and open source
00:45:56 - Will observability engineering survive as a role?

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Is your team's review capacity keeping up with your AI coding agents? Tell us in the comments.

HumanLayer CEO Dexter Horthy on why his team's bet on spec driven development nearly wrecked their own codebase, and what changed his mind about reviewing AI-written code. Dexter walks through the "YOLO pull request" experiment that ran for months before the codebase became unusable, the "dumb zone" that limits how much context you can hand a model, and why he now believes there will always be alpha in reviewing something.

What we cover:
– How HumanLayer's "read the plan, skip the code" experiment quietly broke their own codebase
– Why context windows have a "dumb zone," and what that means for context engineering
– Sean Grove's idea that specs, not code, are becoming the durable artifact
– Building overnight agents that review and fix code before a human ever sees it
– Why treating software development like a factory changes how bugs compound
– What Dexter thinks his advice on reviewing AI-written code will look like in 12 months

Chapters:
00:00:00 - Introduction
00:02:15 - Meet Dexter Horthy, CEO of HumanLayer
00:06:03 - The "dumb zone": why more context makes models dumber
00:06:40 - Sean Grove's "the spec is the new code"
00:09:22 - The YOLO pull-request experiment that broke their codebase
00:11:09 - Letting the model own the architecture
00:17:03 - Planning as expected-pain management
00:28:15 - Slop Code Bench and the maintainability oracle problem
00:37:59 - Why software factories aren't like car factories
00:42:49 - "There will always be alpha in reviewing something"

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Where do you land on the determinism-to-adaptability slider — plan tightly, or let the agent run? Tell us in the comments.

Spec-driven development is reshaping what software engineers actually do all day, and Marc Brooker, VP and Distinguished Engineer at AWS, has read 3,000 to 4,000 postmortems that convinced him the code was never the hard part. In this episode of The AI Native Dev, Marc explains why testing and specification are now the real engineering work, why metastable failures keep taking down systems that look perfectly healthy, and why he still won't let AI write a single word of his blog.

What we cover:
– Why spec-driven development is turning testing into the hardest part of software
– What metastable failures are, and why they keep taking healthy-looking systems down
– How agentic coding tools can learn from postmortems and build their own memory
– Why classic authorization breaks down once you're writing agentic policy
– Why Marc won't let AI write his blog, but is fully comfortable with AI-generated code
– What it takes to bring junior engineers up to speed in an AI-native industry

Chapters:
00:00:00 - Introduction
00:02:17 - Marc Brooker: 18 years at AWS building agentic dev tools
00:04:36 - Inside Strands, AWS's open source agent SDK
00:06:44 - Fifteen years on call, and what agents still can't debug
00:11:23 - Metastable failures and the humility of 4,000 postmortems
00:14:32 - Teaching agents to learn from postmortems and build their own memory
00:20:14 - Why agentic policy needs more than classic authorization
00:32:14 - The agentic software development hypothesis: spec-driven development, oracles, and testing
00:39:56 - Why Marc won't let AI write his blog (but will let it write code)
00:46:37 - Advice for leveling up junior engineers in the AI era

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Do you trust an agent to run its own on-call rotation yet? Tell us in the comments.

Coding agent reliability isn't a model problem, it's a gates problem. Ran Aroussi, creator of yfinance and founder of Automaze, argues that an "agentic workflow" is a contradiction in terms, and that the only thing standing between you and full autonomy is deciding where a human still signs off. He also thinks you stopped needing junior developers for their coding skills about a year ago, and that hiring them anyway is the only way the industry gets its next generation of architects.

What we cover:
– Why "agentic workflow" is a contradiction, and when agentic coding is the wrong tool for the job
– Where a human still has to sign off, and what coding agent reliability actually depends on
– What a software factory changed about delivery times, pricing and the client backlog
– Why you no longer need juniors for their coding skills, and why you should hire them anyway
– Teachable knowledge vs earned knowledge, and the experience agents cannot compress
– The law firm model: why dev agencies may end up looking more like Kirkland & Ellis than a SaaS startup

Chapters:
00:00:00 - Introduction
00:03:05 - yfinance, open source and 30 million downloads a month
00:06:03 - Automaze and the MUXI agent application server
00:10:42 - Where to draw the line on agent autonomy
00:15:12 - Software factories and fully autonomous merges
00:22:25 - The architect archetype and the four-phase ladder
00:28:11 - Teachable knowledge vs earned knowledge
00:30:14 - What agents did to delivery times and pricing
00:37:03 - Why "agentic workflow" is an oxymoron
00:41:08 - Anthropic, OpenClaw and the end of flat subscriptions

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Where's your gate? Tell us in the comments where you still refuse to let a coding agent merge without a human looking.

The software factory that put up 850 pull requests in a week, with 85 to 90% of them handled by agents end to end. Tessl's Head of Product Dru Knox on how it actually got built, why it started with skills rather than automation, and the two things nobody expects a factory to change. Guy Podjarny digs into the skills-to-loops-to-factory path, and what breaks when you skip a step.

What we cover:
– What a software factory actually is, and how it differs from a pile of disconnected automations
– Loop engineering: turning a skill into something that gets better every time it runs
– Why context engineering has to come before automation, and what stalls when it doesn't
– The two gains nobody forecasts from extra agent capacity: quality and fungibility
– Verifiers and evals, or how you enforce a standard that agents are free to ignore
– Context-driven code review, and why a general-purpose reviewer plateaus

Chapters:
00:00:00 - Introduction
00:03:23 - Kikimora: Tessl's own dark factory
00:04:45 - Skills, loops and factories, defined
00:09:09 - 850 PRs a week, and the road to it
00:11:27 - The surprises: quality and fungibility
00:13:16 - Why a factory has to be context centric
00:16:40 - Context-driven code review and the wall teams hit
00:22:51 - Skills inventory across your whole code estate
00:28:38 - Verifiers, evals and enforcing what you wrote down
00:37:30 - Loops, automations and owning your software factory

Build your software factory, one workflow at a time, with Tessl:
https://tessl.co/coi

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If you've hit the plateau Dru describes, tell us where it stopped compounding for you.

Lada Kesseler's approach to loop engineering is brutally simple: give the agent one step, make it write the result to a file, then read it back and go again. She calls it a centrifuge. Spin fast enough and the stupid comes out.

The Logic20/20 principal engineer has spent the last year and a half building the ground rules, skills and refinement loops that fight the defaults coding agents ship with, starting with an instruction to stop trying to please her. Along the way she explains why she trusts AI-written tests less than AI-written code, why the description field in an agent skill was never meant for humans, and why most teams chasing a software factory are about to drown in garbage.

What we cover:
– Why agent ground rules have to give the model a mission to disagree with you
– The refinement loop that gets to quality when the first attempt never does
– Why an agent harness only works if it does one thing at a time
– Sketch prototypes: replacing your code with a markdown file and an agent
– Why AI-generated tests are more dangerous than AI-generated code
– Software factories, and what happens when you try to switch into one overnight

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Chapters:
00:00:00 - Introduction
00:03:38 - Using AI for everything, not just code
00:06:15 - Ground rules: fixing the defaults agents ship with
00:10:02 - Agent skills and why the description isn't for humans
00:13:09 - Loop engineering: how you actually get to quality
00:16:03 - The biggest misconception about coding agents
00:20:43 - Sketch prototypes and the limits of AI architecture
00:27:48 - Reverse direction: you are the decider
00:31:58 - Why she trusts AI tests less than AI code
00:35:00 - Software factories, or drowning in garbage

Build your software factory, one workflow at a time, with Tessl:
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Where are you on the loop engineering curve, still hoping for a good first try or already spinning? Tell us in the comments.

Turning every repo into a software factory sounds like marketing until you see the machinery underneath. Don Syme, Principal Researcher at GitHub, breaks down continuous AI and GitHub Agentic Workflows, now in public preview. The short version: the agents are the easy part, the quality gates and guardrails are the work.

Watch Don's AI DevCon Talk here: https://youtu.be/kbvqRWY-bUs

What we cover:
– What continuous AI is, and why it sits beside CI/CD rather than inside it
– Why bounding the context is what stops automated agents going off the rails
– Whether the repo is really the right unit for a software factory, and where monorepos break it
– One workflow or an agent zoo? Cost control, scheduling and model exams
– Quality gates, human review, and equipping the reviewer instead of drowning them

Chapters:
00:00:00 - Introduction
00:03:52 - What is continuous AI?
00:07:33 - From single player to multiplayer automation
00:09:42 - Bounding the context so agents don't go off the rails
00:12:57 - Software factories, loops and harnesses
00:18:44 - Inside GitHub Agentic Workflows
00:25:19 - Why the repo became the unit of production
00:37:11 - What belongs in the repo, and what doesn't
00:45:07 - One workflow or an agent zoo? Cost control in the factory
00:52:01 - Quality gates, human review and equipping the reviewer

🌐 Tessl: https://tessl.io
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If you're building a software factory of your own, tell us in the comments where your bottleneck actually sits.

The thing that makes an agent useful is the exact thing that makes it dangerous. Keycard co-founder Ian Livingstone breaks down why non-determinism is both the feature and the bug, and why identity, not model quality, is what really gates how much autonomy you can hand an agent. If you have ever clicked "always allow" without reading it, this one is about you.

What we cover:
– Why authentication was enough in the cloud era and stops being enough with agents
– The background check you can't run on an agent, and what has to replace it
– Mission identity: who is acting, on whose behalf, and for what purpose
– Cross App Access, Agent Auth and the protocols trying to fix OAuth
– Consent fatigue, LLM as a judge, and where hard boundaries still belong
– Why MCP ships with an auth story and CLI tools don't

Chapters:
00:00:00 - Introduction
00:03:43 - Why every wave of computing rewrites identity
00:04:50 - The feature and the bug are the same thing
00:10:23 - Why shared secrets break for agents
00:13:36 - The background check you can't run on an agent
00:19:42 - Chargebacks, delegation and proving intent
00:22:16 - Mission identity: a new layer
00:28:07 - Cross App Access, Agent Auth and emerging protocols
00:33:02 - MCP vs CLI, and how Keycard works
00:43:22 - Identity three years from now

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Where do you draw the hard line for your own agents? Tell us in the comments.

Datadog's Language Foundations team deleted an entire folder of AI context files that had been carefully written and maintained for over a year, expecting a performance hit. Instead, their evals got better. Simon Boudrias, who runs Language Foundations at Datadog, walks Guy through what that taught his team about context rot, and the full journey of scaling AI coding agents to 4,000 engineers.

What we cover:
– How Datadog scaled Cursor and Claude Code to 4,000 engineers in under a year
– Why Datadog deleted all of its AI context files, and what happened to eval scores
– Building an eval-driven code review system that replays old PRs to catch real incidents
– Where open-weight models like GLM 5.2 stand against the frontier
– Rethinking hiring and career ladders now that AI can run a real codebase interview

Chapters:
00:00:00 - Introduction
00:03:19 - Simon's role and Datadog's 4,000-engineer org
00:04:31 - The Cursor rollout that took off overnight
00:07:53 - How Claude Code entered the picture
00:10:51 - Building dedicated Signals and Flows teams
00:11:58 - Why Datadog bet early on evals
00:33:44 - Deleting all their AI context and getting better evals
00:47:51 - Where open-weight models stand today
00:51:45 - Rethinking hiring and career ladders for AI
00:59:24 - The real prize: better decisions, not just productivity

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What's the oldest file in your AGENTS.md or CLAUDE.md that you're afraid to delete? Tell us in the comments.