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The AI Native Dev – from Copilot today to AI Native Software Development tomorrow — Technology
The AI Native Dev – from Copilot today to AI Native Software Development tomorrow

Welcome to The AI Native Developer, hosted by Guy Podjarny and Simon Maple. Join us as we explore and help shape the future of software development through the lens of AI. In this new paradigm of AI Native Software Development, we delve into how AI is transforming the way we build software, from tools and practices to the very structure of development teams.

Our target audience includes developers and development leaders eager to stay ahead of the curve. If you’re passionate about the future of software development and curious about how to leverage AI to build effective teams and groundbreaking software, this podcast is for you.

Each week, we bring you insights into the latest AI tools and best practices, keeping you up-to-date with the cutting-edge advancements in the industry. Additionally, every two weeks, we present deep dives with experts and leaders in the AI and software development space, offering a glimpse into the future of AI development.

Tune in to discover how AI will revolutionize your workflows, roles, and organizations. Get inspired by the latest tools and best practices, and prepare to be part of the next generation of software development.

Latest Episodes:

Datadog Deleted All Its AI Context. It Worked. Aug 04, 2026

Show notes

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
🔔 Subscribe for weekly episodes on AI-native development
What's the oldest file in your AGENTS.md or CLAUDE.md that you're afraid to delete? Tell us in the comments.

Inside the Dark Factory: AI That Ships Code Solo Jul 28, 2026

Show notes

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
🔔 Subscribe for weekly episodes on AI-native development
What would your own verification layer catch, and where would it break? Let us know in the comments.

BONUS: Snyk Found Malware Inside AI Agent Skills Jul 23, 2026

Show notes

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.

From Living Room Hack to 30 AI Agents at Cyera Jul 21, 2026

Show notes

An engineer builds an AI agent to manage his own life, decides an unrestricted "does everything" agent is too dangerous to trust, and ends up creating the internal agent platform that now runs 30 agents across his entire company.
Ori Shoshan, tech lead at Cyera, walks through the guardrails, citation system, and knowledge graph that turned "let the agent do anything" into "let the agent do exactly what it's supposed to, and nothing else."
What we cover:
– Why one engineer built his own AI agent in his living room, and how it grew into Cyera's internal agent platform
– Whitelisting tools instead of blacklisting them, plus the "escape hatch" that keeps agents honest
– Backing every claim with a citation and using a second model to catch hallucinations before they reach a human
– Trading RAG for a knowledge graph the agent can walk like a wiki
– Turning "use this platform" into "build your own agent" to drive adoption across an entire engineering org
– Running agents on confidential data that can investigate everything but can only ever say what's been cleared
Chapters:
00:00:00 - Introduction
00:02:04 - Meet Ori Shoshan and what Cyera does
00:06:50 - The living-room spark: why Ori built his own agent
00:11:09 - Whitelisting tools instead of blacklisting them
00:12:55 - Why every claim needs a citation
00:15:35 - Reducing hallucinations with clean-context verification
00:21:04 - Taking Borg to Slack: the first agents at work
00:25:33 - Why naming your agent drives adoption
00:39:53 - Knowledge graphs over RAG
00:52:00 - The AI Captains: scaling adoption with carrots, not sticks
🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native development
Have you built guardrails like this into your own agents? Let us know what's worked (or blown up) for you in the comments.

Patrick Debois Maps the Patterns of AI-Native Dev Jul 14, 2026

Show notes

After DevOps, Patrick Debois has a new map. The godfather of DevOps returns to walk through his AI patterns research: how he tracks where AI-native development is heading by mining social signals instead of surveys, why the stack is settling into patterns worth learning, and why your AI budget problem is really an optimization problem.
What we cover:
– Inside the AI patterns site at Tessl.io/patterns and how it's auto-generated
– The four layers of AI enablement: agent, team, platform, and organization
– Solo, shared, and multiplayer modes: the compounding effect of shared context
– Managing agent costs: budgets as a forcing function for optimization
– Scaling adoption: find the success story, don't fight the skeptics
– Hiring system thinkers instead of elegant coders
Chapters:
00:00:00 - Introduction
00:02:28 - Why AI grips Patrick Debois
00:04:33 - Agent, team, platform and org enablement
00:06:36 - Is AI-native development maturing?
00:11:19 - Inside the AI patterns site
00:18:14 - Quality, security and the IDE as review interface
00:22:45 - Changing roles and harness engineering
00:26:32 - Hiring in the AI era
00:30:31 - Scaling adoption across the org
00:42:03 - Cost management and ROI
🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native development
Where is your organization on the maturity curve? Drop your take in the comments.

Inside Anthropic: How Claude Tag Is Changing Agentic Work Jul 07, 2026

Show notes

Six people reacted to Boris's side-project Slack post. A year later, Claude Code is ubiquitous, and the company just launched its next evolution: Claude Tag, an AI teammate that lives in Slack.
Lamis Mukta, Member of Technical Staff at Anthropic, joins Simon Maple to unpack how Claude Tag works, why Anthropic built it, and what it took internally to go from a scrappy side project to a company-wide habit.
What we cover:
– What Claude Tag actually is, and how it differs from Claude Code and Cowork
– Why trust in AI agents is really a function of model capability, not just comfort
– The internal "dogfooding" culture that shaped Claude Code and Claude Tag
– How Anthropic secures multiplayer AI with agent identities and channel-level permissioning
– Where Claude and Claude Tag show up outside of engineering at Anthropic
– Dreaming: how Anthropic's managed agents continually improve their own memory
Watch Lamis' talk from AI Native DevCon London 2026 here: https://youtu.be/tTcxVv8HHNw
Chapters:
00:00:00 - Introduction
00:01:46 - Meet Lamis Mukta from Anthropic
00:02:56 - What is Claude Tag?
00:10:07 - From single-player to multiplayer agentic coding
00:15:50 - Trust, capability, and the METR chart
00:21:02 - How the industry is really using Claude Code
00:26:33 - The Claude Code origin story
00:33:47 - Agent identities and permissioning at scale
00:40:23 - Claude beyond engineering at Anthropic
00:48:16 - Dreaming and practical tips for rolling out Claude Tag
🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native development
What's the one thing your team would delegate to an AI teammate first? Let us know in the comments.

The Tessl Agent: Build Your Software Factory on Autopilot Jun 30, 2026

Show notes

What if the whole point of your AI agent was to eventually make itself redundant? Dru Knox, Head of Product at Tessl, introduces the Tessl agent — a new interface built not just for AI-assisted coding, but for building the software factory that keeps improving without constant human input.
This is a conversation about loop engineering: how to set up automated feedback cycles so your agents get smarter, your code review gets tighter, and your team ships more without adding more toil.
What we cover:
– What the Tessl agent is and how it fits into the broader Tessl platform
– Loop engineering: why building automated feedback loops is the right place to start
– How the agent sets up and continuously improves agentic code review
– Why optimising your AI agent costs is usually the wrong lever to pull
– The case for open, modular software factories — and the risk of vendor lock-in
– UX expectations in the AI era: why outcome-oriented interfaces are now the baseline
🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native development
What's your approach to building feedback loops into your agent workflows? Drop a comment — we'd love to hear how teams are thinking about this.

Why Agents Are Forcing Enterprises to Finally Fix Their Dev Process Jun 25, 2026

Show notes

Enterprises are finally being forced to care about their software development lifecycle — not because anyone suddenly got disciplined, but because agents cost money and the waste is now visible. When it was humans, it was "Timmy's just lazy." Now it's a line item.

Simon Maple sat down with Patrick Debois (the godfather of DevOps, now DevRel at Tessl), Tammuz Dubnov (co-founder and CEO of Autonomy AI), and Daniel Jones (Head of Product at re:cinq) at AI Native DevCon London for a wide-ranging panel on AI enablement — who owns it, what's breaking, and what the organisations getting it right are actually doing differently.

What we cover:

– Who should own agentic coding adoption inside an enterprise, and why platform teams are already filling the vacuum

– The "Timmy's lazy" problem: why agent cost visibility is forcing process discipline that humans never got

– Why PR-based workflows are an anti-pattern inside enterprises once you're moving at agent speed

– The PUMP framework (Plan, merge, polish): how one team is shipping features with developers, PMs, and designers all opening PRs –

Rethinking what a "test" is in an agentic world — and why feedback loops matter more than first-pass correctness

– The biggest mistake enterprises are making right now: piecemeal adoption with no mandate and no shared tooling

🌐 Tessl: https://tessl.io

🔔 Subscribe for weekly episodes on AI-native development

What's your team's approach to AI enablement — central mandate or letting individuals find their own way? Drop it in the comments.

BONUS: DevCon London: Real Talk on AI ROI, Harnesses & Evals Jun 23, 2026

Show notes

From the expo floor of AI Native DevCon London, Simon Maple went straight to the developers — speakers, attendees, and sponsors — to ask what's actually working with AI in 2026. The verdict? Outcomes beat outputs every time, 4,000-hour workloads are collapsing to 20 minutes, and the real bottleneck isn't code.
This is a conference floor walkthrough: honest, unscripted takes on harness engineering, evals, AI adoption mindsets, and the change management challenge that nobody talks about enough.
What we cover:
– Why measuring token usage, code commits, and outputs will lead your team astray
– How NearForm cut a 4,000-hour AML backlog to 20 minutes using agents
– Harness engineering and evals as the developer skills that matter most in 2026
– Why change management — not tooling — is the missing ingredient for real AI ROI
– How AutonomyAI is letting PMs and designers ship directly to production
Chapters:
00:00:00 - Welcome to AI Native DevCon London
00:01:03 - Chris Baty: Outcomes Over Outputs
00:05:15 - Martin: How Tooling Changed Everything
00:07:21 - Ryan: Harnesses, Evals and Skills
00:08:50 - Manny Saka: Plan Before You Prompt
00:11:58 - Cian O Maidin, NearForm: Real AI ROI
00:15:21 - Snyk: Building Trust at Scale
00:17:01 - AutonomyAI: Shipping Without Engineering
00:21:35 - Tessl Agent: Harness Engineering
00:22:41 - Closing Thoughts
🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native development
Whether you're cautious or YOLOing it with AI — drop where you land in the comments.

AI Security & the Agent-Ready Web: Experts Weigh In Jun 16, 2026

Show notes

What does it mean to build securely when agents can negotiate their own guardrails? And what happens to the web — CLIs, frameworks, even the browser itself — when the primary user is no longer human? At AI Native DevCon London, Simon Maple sat down with two panels of experts to find out.

First: a security roundtable with Joseph Katsioloudes from GitHub, Liran Tal from Snyk, and John Groetzinger from Cisco.

Then: a web AI conversation with Dana Lawson from Netlify, Maximiliano Firtman from codemia, and James Moss from Tessl.

What we cover:

– Why 83% of enterprises plan to deploy AI but only 29% feel ready to do so securely

– Prompt injection as a risk you have to accept — and how least privilege and sandboxing are your real defences

– The "agent experience" concept: why systems built for human eyes fail at machine scale

– Whether fundamentals like HTTP, semantic HTML, and accessibility still matter when agents do the heavy lifting

– How WebMCP lets websites expose tools directly to agents

— and why blocking them is like trying to turn off the sun

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