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

Tessl

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.

  1. 1d ago ·  Video

    Caleb Sima: AI Attackers Move at Machine Speed

    AI attackers now move at machine speed, while most security teams still move at meeting speed. Former Robinhood CSO Caleb Sima has seen an AI attacker up close, and he argues the only way to keep up is to take humans out of the defense loop. Caleb joins Guy Podjarny to unpack what changes when the attacker is an agent: one that hits every surface at once, reverses open-source code to find a 0-day, and once inside, burns your own AI tokens to keep going. What we cover: – Why AI attackers outran the red teamers' predictions, including 400 Linux 0-days found at 2.5 hours each – Whether sandboxes can contain AI agents, and why external monitoring matters – Who is accountable when your AI agent hacks someone else – What actually changes when an AI attacker gets inside your company – Deception, digital twins and forensic-grade detection – Automating security: instant patching, one-way vs two-way doors, and security teams that ship the fix Chapters: 00:00:00 - Introduction 00:02:58 - AI security goes mainstream 00:05:52 - Why AI attackers surprised the red teamers 00:12:50 - Can sandboxes contain AI agents? 00:18:03 - Security vs safety vs alignment 00:22:18 - Who's accountable when your AI agent hacks? 00:29:00 - What changes when an AI attacker hits you 00:36:47 - Detection, forensics and deception 00:44:10 - Machine speed vs meeting speed: automating defense 00:48:53 - Security teams that ship the fix Build your software factory, one workflow at a time, with Tessl: https://tessl.co/9ja 🔔 Subscribe for weekly episodes on AI-native development Are your defenses ready for AI attackers, or still running at meeting speed? Tell us in the comments.

    Caleb Sima: AI Attackers Move at Machine Speed

  2. Oct 22 ·  Bonus Video

    Your Coding Agent Should Never Hold a Credential

    Your coding agent should never hold a credential, and in a software factory that rule is only the start. Recorded live at the AI Security Summit in San Francisco, Simon Maple sits down with Eli Aleyner (Docker), Marcelo Sousa (Snyk) and Ian Livingstone (Keycard), co-authors of Agent Baseline, a security reference architecture for the AI software factory. The panel unpacks why agents have to be treated as fundamentally unpredictable actors, why sandboxing alone is not enough when skills and MCP servers make the supply chain dynamic, and why approval prompts collapse into fatigue. Marcelo's verdict: sometimes these are kids with chainsaws. What we cover: – The three levels of autonomy, from coding agents to fully agent-directed workflows – Sandboxing coding agents and injecting secrets at the egress point – Why the agent supply chain is dynamic: skills, MCP servers and cloned repos – Prompt injection, least privilege and agents monitoring other agents in the harness – Approval fatigue: YOLO mode vs CYA mode – Where to start securing your software factory on Monday morning Chapters: 00:00:00 - Introduction 00:03:14 - What Agent Baseline is and who it's for 00:04:17 - The three levels of software factory autonomy 00:06:53 - Sandbox boundaries and manufacturing trust in agents 00:09:22 - Why coding agents should never hold credentials 00:11:44 - The dynamic agent supply chain: skills and MCP servers 00:14:57 - Untrusted input, least privilege and agents watching agents 00:18:02 - Approval fatigue: YOLO mode vs CYA mode 00:20:22 - Are poisoned agent skills a supply chain problem? 00:22:45 - Where the panel disagreed, and where to start Monday Build your software factory, one workflow at a time, with Tessl: https://tessl.co/gsh 🔔 Subscribe for weekly episodes on AI-native development Which level of the software factory are you at today, and what are you locking down first? Tell us in the comments.

    Your Coding Agent Should Never Hold a Credential

  3. Oct 20 ·  Video

    Snyk's Manoj Nair: Skills and MCP Are the New npm

    Skills and MCP servers are the new npm, and AI coding agents are pulling them into enterprise codebases faster than security can keep up. Manoj Nair, Chief Innovation Officer at Snyk, joins Guy Podjarny at the Snyk AI Security Summit to map where enterprise agentic coding really stands. They get into why a 50,000-agent Claude Code rollout was pulled forward by weeks, why the autonomous attacker has some CISOs ready to accept an outage to patch faster, and how security is moving into the agent loop of the software factory. What we cover: – Why leadership and culture, not tooling, decide how fast enterprises adopt AI coding agents – The three risks of agentic coding: insecure output, the skills and MCP server supply chain, and agent behavior – Running coding agents locked down: sandboxes, agent identities and no open internet – Who owns the code an agent writes, and why AI-native organizations treat it as shared risk – Securing the software factory: security context in the agent loop and agentic remediation of the backlog – What still has to be solved before enterprises trust agents: identity, intent and behavior Chapters: 00:00:00 - Introduction 00:01:50 - The state of enterprise AI and agent adoption 00:09:45 - Leadership, culture and a 50,000-agent rollout 00:11:18 - Agentic coding risks: output, supply chain, behavior 00:14:29 - Locked-down coding agents and the security trade-off 00:18:01 - Who owns agent output? Compliance and accountability 00:22:54 - The autonomous attacker and security technical debt 00:27:56 - Securing the product vs securing the software factory 00:33:00 - Agentic remediation and the security backlog 00:40:18 - What enterprises need to trust AI coding agents Build your software factory, one workflow at a time, with Tessl: https://tessl.co/jw0 🔔 Subscribe for weekly episodes on AI-native development Where is your organization on the path from copilot to AI coding agents? Tell us in the comments.

    Snyk's Manoj Nair: Skills and MCP Are the New npm

  4. Oct 13 ·  Video

    Maria Gorinova: Feedback Loops That Fix Your Codebase

    Agent feedback loops are how a software factory maintains itself: agents mining their own session logs, PR comments and production signals, then fixing what they find. Maria Gorinova, member of technical staff at Tessl, explains how to build them, and why vague intent turns a loop into a mess. What we cover: – What a software factory is: repeatable workflows, a shared execution environment and agent feedback loops – Why agent context alone isn't enough, and where harness engineering comes in – The three stages of a feedback loop: analyze a data stream, find the problem, request the fix – Inner, middle and outer loops, from verifiers on every PR to self-healing in production – How Tessl's "comment diet" loop catches drift that no single PR causes – Operators and stewards: the human roles in a software factory Chapters: 00:00:00 - Introduction 00:02:36 - What is a software factory? 00:05:38 - From agents to context to harness engineering 00:09:15 - The three stages of an agent feedback loop 00:13:16 - Inner, middle and outer feedback loops 00:18:35 - Why vague intent breaks agent feedback loops 00:21:50 - Comment bloat, drift and the rule of three 00:25:48 - The software factory mindset 00:27:45 - Loops, feedback loops and meta feedback loops 00:32:45 - Operators, stewards and building your first loop Build your software factory, one workflow at a time, with Tessl: https://tessl.co/2uf 🔔 Subscribe for weekly episodes on AI-native development Which agent feedback loop would you build first in your own software factory? Tell us in the comments.

    Maria Gorinova: Feedback Loops That Fix Your Codebase

  5. Oct 6 ·  Video

    Legora CTO: Why AI Agents Need More Than a Chat Window

    AI coding agents now write almost all of Legora's code, with engineers running up to ten in parallel. Yet the CTO behind that shift thinks measuring adoption by tokens burned is a mistake. Jacob Lauritzen, CTO of Legora, joins Simon Maple to unpack what actually scales when agents do the typing, and where human judgment still has to stay in charge. What we cover: – How Legora's engineers orchestrate many AI coding agents at once, from shared local dev resources to cloud agent setups – An agentic loop that reproduces, fixes and tests bugs straight from Slack – Why token maxing is a bad way to drive AI adoption, and what to measure instead – Agent evaluation: regression testing the harness as models and prompts change every week – Decision models, the verifier's rule, and where autonomy ends and human judgment begins – Why AI agents need more than a chat interface Chapters: 00:00:00 - Introduction 00:01:47 - Legora's growth from $100M to $200M ARR 00:03:44 - Parallel coding agents and bug-fix loops 00:07:15 - Senior engineers vs AI-first engineers 00:10:10 - Why token maxing is the new lines of code 00:14:13 - What AI can and can't verify in legal work 00:17:30 - Decision models vs autoregressive LLMs 00:24:22 - Weekly model benchmarks and harness evals 00:26:58 - Why AI agents need more than a chat interface 00:35:03 - The verifier's rule and litigation strategy Build your software factory, one workflow at a time, with Tessl: https://tessl.co/lpg 🔔 Subscribe for weekly episodes on AI-native development Is token count telling you anything real about how your team uses AI coding agents? Tell us in the comments.

    Legora CTO: Why AI Agents Need More Than a Chat Window

  6. Sep 29 ·  Video

    Liz Fong-Jones: 2x the PRs, 1.5x the Incidents

    Honeycomb went from 30 to 70 merged pull requests a day in three months. The catch: automated code review, not code generation, became the real bottleneck of software automation. Liz Fong-Jones, Technical Fellow at Honeycomb, explains why incidents still rose 1.5x, how their internal bot Autobot now reviews every PR, and why AI amplifies whatever org you already have. What we cover: – How automated code review lets humans focus on design, not trivial bugs – Using a decision model like Jev to decide which PRs are safe to auto-merge – What makes a codebase ready for AI coding agents – When to trust AI agents with production incidents, and when they're just throwing darts – Why "Claude did it" isn't an excuse, and what ownership means with AI – How open source maintainers can handle a flood of AI slop pull requests Chapters: 00:00:00 - Introduction 00:06:41 - Why AI amplifies dysfunctional engineering orgs 00:10:45 - What makes a codebase AI ready 00:14:01 - Honeycomb's Autobot and automated code review 00:19:06 - Using Jev to decide which PRs are safe to merge 00:26:51 - Trusting AI agents during production incidents 00:30:40 - Least privilege and guardrails for coding agents 00:33:25 - If your name's on it, you own it 00:38:55 - AI slop pull requests and open source 00:45:56 - Will observability engineering survive as a role? Build your software factory, one workflow at a time, with Tessl: https://tessl.co/nlr 🔔 Subscribe for weekly episodes on AI-native development Is your team's review capacity keeping up with your AI coding agents? Tell us in the comments.

    Liz Fong-Jones: 2x the PRs, 1.5x the Incidents

  7. Sep 22 ·  Video

    Dexter Horthy: Why We Stopped Trusting AI to Write the Plan

    HumanLayer CEO Dexter Horthy on why his team's bet on spec driven development nearly wrecked their own codebase, and what changed his mind about reviewing AI-written code. Dexter walks through the "YOLO pull request" experiment that ran for months before the codebase became unusable, the "dumb zone" that limits how much context you can hand a model, and why he now believes there will always be alpha in reviewing something. What we cover: – How HumanLayer's "read the plan, skip the code" experiment quietly broke their own codebase – Why context windows have a "dumb zone," and what that means for context engineering – Sean Grove's idea that specs, not code, are becoming the durable artifact – Building overnight agents that review and fix code before a human ever sees it – Why treating software development like a factory changes how bugs compound – What Dexter thinks his advice on reviewing AI-written code will look like in 12 months Chapters: 00:00:00 - Introduction 00:02:15 - Meet Dexter Horthy, CEO of HumanLayer 00:06:03 - The "dumb zone": why more context makes models dumber 00:06:40 - Sean Grove's "the spec is the new code" 00:09:22 - The YOLO pull-request experiment that broke their codebase 00:11:09 - Letting the model own the architecture 00:17:03 - Planning as expected-pain management 00:28:15 - Slop Code Bench and the maintainability oracle problem 00:37:59 - Why software factories aren't like car factories 00:42:49 - "There will always be alpha in reviewing something" Build your software factory, one workflow at a time, with Tessl: https://tessl.co/4ep 🔔 Subscribe for weekly episodes on AI-native development Where do you land on the determinism-to-adaptability slider — plan tightly, or let the agent run? Tell us in the comments.

    Dexter Horthy: Why We Stopped Trusting AI to Write the Plan

  8. Sep 16 ·  Video

    AWS's Marc Brooker: Specs, Not Code, Are the Hard Part

    Spec-driven development is reshaping what software engineers actually do all day, and Marc Brooker, VP and Distinguished Engineer at AWS, has read 3,000 to 4,000 postmortems that convinced him the code was never the hard part. In this episode of The AI Native Dev, Marc explains why testing and specification are now the real engineering work, why metastable failures keep taking down systems that look perfectly healthy, and why he still won't let AI write a single word of his blog. What we cover: – Why spec-driven development is turning testing into the hardest part of software – What metastable failures are, and why they keep taking healthy-looking systems down – How agentic coding tools can learn from postmortems and build their own memory – Why classic authorization breaks down once you're writing agentic policy – Why Marc won't let AI write his blog, but is fully comfortable with AI-generated code – What it takes to bring junior engineers up to speed in an AI-native industry Chapters: 00:00:00 - Introduction 00:02:17 - Marc Brooker: 18 years at AWS building agentic dev tools 00:04:36 - Inside Strands, AWS's open source agent SDK 00:06:44 - Fifteen years on call, and what agents still can't debug 00:11:23 - Metastable failures and the humility of 4,000 postmortems 00:14:32 - Teaching agents to learn from postmortems and build their own memory 00:20:14 - Why agentic policy needs more than classic authorization 00:32:14 - The agentic software development hypothesis: spec-driven development, oracles, and testing 00:39:56 - Why Marc won't let AI write his blog (but will let it write code) 00:46:37 - Advice for leveling up junior engineers in the AI era Build your software factory, one workflow at a time, with Tessl: https://tessl.co/[SLUG] 🔔 Subscribe for weekly episodes on AI-native development Do you trust an agent to run its own on-call rotation yet? Tell us in the comments.

    AWS's Marc Brooker: Specs, Not Code, Are the Hard Part
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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.

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