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

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1. Maggie Appleton: Agentic Coding Has Outgrown the Text Box

   3 november 2026 · 52 min

   Afspelen
   Agentic coding has outgrown the text box. Maggie Appleton, staff research engineer at GitHub Next, argues that walls of CLI output and AGENTS.md files serve coding agents far better than the humans steering them. She walks through Chopin, the GitHub Next prototype where background agents turn a team's planning chat into a structured plan, and makes the case for agents that prompt you instead of waiting to be prompted. Lees meer

   What we cover:
   – Why text-heavy agentic coding tools favor agents over humans
   – Boundary objects: what AGENTS.md, skill files and MCP servers get right and wrong
   – Multiplayer planning with coding agents in GitHub Next's Chopin
   – Human-to-agent handoffs, formal verification and spec-driven development
   – Why software quality is rising, not falling, with coding agents
   – Which roles still matter: PMs, designers and engineers

   Chapters:
   00:00:00 - Introduction
   00:02:14 - Inside GitHub Next and the Chopin project
   00:05:57 - Why solo agents break team alignment
   00:07:52 - Why coding agents struggle with design
   00:12:16 - Boundary objects: AGENTS.md, skills and MCP
   00:18:27 - Planning with coding agents as collaborators
   00:26:49 - Multiplayer planning with background agents
   00:31:57 - Agent handoffs and spec-driven development
   00:37:41 - Quality, craft and who decides what to build
   00:44:29 - Starting a career in agentic coding

   Build your software factory, one workflow at a time, with Tessl:
   https://tessl.co/j3d

   🔔 Subscribe for weekly episodes on AI-native development

   Should coding agents be prompting you more? Tell us in the comments how your team plans agentic coding work today.

2. Caleb Sima: AI Attackers Move at Machine Speed

   27 oktober 2026 · 55 min

   Afspelen
   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. Lees meer

   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.

3. Your Coding Agent Should Never Hold a Credential

   22 oktober 2026 · 30 min

   Afspelen
   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. Lees meer

   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.

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

   20 oktober 2026 · 47 min

   Afspelen
   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. Lees meer

   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.

5. Maria Gorinova: Feedback Loops That Fix Your Codebase

   13 oktober 2026 · 38 min

   Afspelen
   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. Lees meer

   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.

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

   6 oktober 2026 · 44 min

   Afspelen
   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. Lees meer

   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.

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

   29 september 2026 · 50 min

   Afspelen
   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. Lees meer

   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.

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

   22 september 2026 · 56 min

   Afspelen
   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. Lees meer

   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.

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

   16 september 2026 · 57 min

   Afspelen
   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. Lees meer

   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.

10. You Don't Need Juniors to Code. Hire Them Anyway.

    8 september 2026 · 52 min

    Afspelen
    Coding agent reliability isn't a model problem, it's a gates problem. Ran Aroussi, creator of yfinance and founder of Automaze, argues that an "agentic workflow" is a contradiction in terms, and that the only thing standing between you and full autonomy is deciding where a human still signs off. He also thinks you stopped needing junior developers for their coding skills about a year ago, and that hiring them anyway is the only way the industry gets its next generation of architects. Lees meer

    What we cover:
    – Why "agentic workflow" is a contradiction, and when agentic coding is the wrong tool for the job
    – Where a human still has to sign off, and what coding agent reliability actually depends on
    – What a software factory changed about delivery times, pricing and the client backlog
    – Why you no longer need juniors for their coding skills, and why you should hire them anyway
    – Teachable knowledge vs earned knowledge, and the experience agents cannot compress
    – The law firm model: why dev agencies may end up looking more like Kirkland & Ellis than a SaaS startup

    Chapters:
    00:00:00 - Introduction
    00:03:05 - yfinance, open source and 30 million downloads a month
    00:06:03 - Automaze and the MUXI agent application server
    00:10:42 - Where to draw the line on agent autonomy
    00:15:12 - Software factories and fully autonomous merges
    00:22:25 - The architect archetype and the four-phase ladder
    00:28:11 - Teachable knowledge vs earned knowledge
    00:30:14 - What agents did to delivery times and pricing
    00:37:03 - Why "agentic workflow" is an oxymoron
    00:41:08 - Anthropic, OpenClaw and the end of flat subscriptions

    Build your software factory, one workflow at a time, with Tessl:
    https://tessl.co/vut

    🔔 Subscribe for weekly episodes on AI-native development

    Where's your gate? Tell us in the comments where you still refuse to let a coding agent merge without a human looking.

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