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

The best agentic developers throw away their agent's work without guilt, run three agents at once and only use one, and treat their AI like a junior developer they genuinely dislike. It sounds wrong. It works.

Daniel Jones, Head of Product at re:cinq, has upskilled hundreds of developers across Northern Europe's largest enterprises. In this episode he joins Simon Maple to share the counterintuitive habits, hard data, and practical frameworks behind high-performing agentic development teams.

On the docket:
Why bad engineering practices get worse, not better, with AI agents
The exact conditions that make your agent hallucinate every time
Why your AGENTS.md is quietly working against you
How to manage context before it kills your productivity
What enterprise AI rollout actually looks like at scale
Why the worst managers get the most out of agentic coding

If your team is adding AI and wondering why things aren't getting faster, this episode is for you.

Connect with us here:
Daniel Jones: https://www.linkedin.com/in/danieljoneseb/
Simon Maple: https://www.linkedin.com/in/simonmaple/
Tessl: https://www.linkedin.com/company/tesslio/
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: [email protected]

Not onboarding your agent is on you.

Richmond Alake, Director of AI Developer Experience at Oracle, joins Simon Maple to make the case that most agent failures come down to one thing: memory. Not the model, not the infrastructure. Memory.

On the docket:
why skills are just SOPs your organisation already has written down
the job title that is replacing prompt engineers
file systems vs databases for agent memory (and why one gets you hacked)
the memory trick that makes agents feel actually intelligent
why the agent loop and training loop are about to become one

The developers who figure this out first are going to be very hard to compete with.

Connect with us here: 
Richmond Alake: https://www.linkedin.com/in/richmondalake/
Simon Maple: https://www.linkedin.com/in/simonmaple/
Tessl: https://www.linkedin.com/company/tesslio/
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: [email protected]

Your AI coding agent learned from millions of lines of code, including insecure ones. That means by default, it can write vulnerable code too.

So how do you fix that?

John Groetzinger, Principal Engineer at Cisco, built CodeGuard, a security skills layer that teaches coding agents how to write and review code securely. He tested it against real scenarios.

The result:
84% success rate vs 47% baseline. Nearly 2× improvement.

In this episode we get into:
how CodeGuard works
why Cisco open sourced it
the surprisingly simple method that gets agents to fix their own mistakes

Try CodeGuard: cisco/software-security on the Tessl registry.

Connect with us here:
John Groetzinger: https://www.linkedin.com/in/john-groetzinger/
Cisco: https://www.linkedin.com/company/cisco/
Simon Maple: https://www.linkedin.com/in/simonmaple/
Tessl: https://www.linkedin.com/company/tesslio/
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: [email protected]

Most teams think agentic dev is about writing better prompts. It's not.

Guy Podjarny and Simon Maple explain why managing context, not crafting prompts, is what separates teams that scale with agents from teams that don't. They walk through a practical framework for building, evaluating, and distributing the context your agents actually need.

In this episode:
• Why agents fail without structured context about your internal platform
• The 3 context layers: policies, platform docs, and application context
• How to build regression evals and torture tests for your agents
• The Context Development Lifecycle (CDLC) - a new loop for agentic dev

Your agents are only as good as the context you give them.
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: [email protected]

"Charts are good for users, not good for agents. Agents look at the underlying data and do deep analysis."

Mirko Novakovic built Instana, sold it to IBM, and now he's building Dash0, rethinking observability for agents, not humans.

In conversation with Guy Podjarny, he explains:
• why OpenTelemetry turned out to be perfect for AI
• how UX changes when agents are your primary users
• why interactive collaboration beats static chat outputs
• the survival question for observability vendors in the AI era

Only 2-3 people in most companies can truly debug production. That knowledge lives in their heads and disappears when they leave. Mirko's betting agents will change that.

Connect with us here:
Mirko Novakovic: https://www.linkedin.com/in/mirkonovakovic/
Dash0: https://www.linkedin.com/company/dash0hq/
Guy Podjarny: https://www.linkedin.com/in/guypo/
Tessl: https://www.linkedin.com/company/tesslio/
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: [email protected]