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

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.

At Tessl, 95% of the code shipped by their internal "Dark Factory" has never been looked at by a human, and the team still ships hundreds of pull requests a week, including through entire weekends. Rob Willoughby, who leads AI engineering at Tessl, joins Simon to open up the hood on how it actually works: the orchestrator, the verification layers, and the failures that forced the team to rebuild trust from scratch.

What we cover:
– How Tessl routes 65-70% of its own pull requests through an autonomous "Dark Factory"
– Why context in the repo matters more to output quality than which model you use
– How natural language "verifiers" turn code review taste into fast, cheap checks agents can pass or fail
– The queue bug that took dozens of pull requests to fix, and the from-scratch Elixir rewrite that stress-tested the whole system
– How to start building your own software factory, one verification layer at a time

Chapters:
00:00:00 - Introduction
00:01:44 - Rob Willoughby joins: Tessl's PR numbers
00:04:01 - Live demo: kicking off two pull requests
00:14:03 - Building the Dark Factory: orchestrator vs. context
00:19:01 - Code review layers: Code Rabbit, Tessl Change Verify, and verifiers
00:29:58 - Earning trust: accountability in an autonomous system
00:33:20 - What broke: the queue bug and the Elixir rewrite experiment
00:39:56 - Onboarding new engineers into the factory
00:42:58 - Advice for teams starting their own software factory
00:52:03 - Back to the demo, and the road to 100% adoption

🌐 Tessl: https://tessl.io
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What would your own verification layer catch, and where would it break? Let us know in the comments.

One Snyk developer's AI skill quietly handed their coding agent production credentials, and the security team found out the hard way. Krzysztof Huszcza, who leads AI security incubation at Snyk, joins this special Tessl and Snyk live stream to unpack the ToxicSkills research that uncovered 76 malicious agent skills in the wild, and what it actually takes to run coding agents safely at scale.

What we cover:
– How Snyk's security team found 76 malicious skills hiding inside a popular open agent skill repository
– Why skills have become the go-to way developers hand context to their coding agents
– The internal incident at Snyk where a developer's skill exposed production credentials to an agent
– How the Tessl and Snyk integration scans every skill and MCP server for risk before you install it
– What's coming next with Snyk's new Evo product for governing coding agents at scale

Chapters:
00:00:00 - Introduction
00:01:33 - Chris's role: AI security incubation at Snyk
00:02:16 - Snyk's roots as a developer-first security company
00:03:58 - New security challenges from AI coding agents
00:06:26 - Skills: the new way to give agents context
00:07:35 - Inside Snyk's ToxicSkills research: malware and prompt injection
00:11:35 - How developers can vet skills before installing them
00:14:38 - A real incident: exposed production credentials at Snyk
00:17:45 - Building a secure-by-default agent stack
00:23:35 - What's next: Snyk's new coding agent security product

🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native development

What's the riskiest skill you've installed without checking it first? Let us know in the comments.