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

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

*AI DevCon is coming back to New York this November*
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https://tessl.io/devcon#tickets

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:
https://tessl.co/k2t

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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

🌐 Tessl: https://tessl.io
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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

🌐 Tessl: https://tessl.io
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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.