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

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
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What's the riskiest skill you've installed without checking it first? Let us know in the comments.
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: podcast@tessl.io

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
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Have you built guardrails like this into your own agents? Let us know what's worked (or blown up) for you in the comments.
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: podcast@tessl.io

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
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Where is your organization on the maturity curve? Drop your take in the comments.
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: podcast@tessl.io

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
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What's the one thing your team would delegate to an AI teammate first? Let us know in the comments.
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: podcast@tessl.io

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.
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: podcast@tessl.io