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

Automated software delivery is moving faster than the people who used to approve every release. LaunchDarkly co-founder and CEO Edith Harbaugh tells Guy Podjarny why rollback should be autonomous, why one-shot coding agents fail, and why the bottleneck has moved to the last mile.

What we cover:
– How automated software delivery and autonomous rollback take humans out of the 2 a.m. incident
– What makes a good feature flag, and when a homegrown system stops being enough
– Why coding agents need an explicit definition of success before they write a line
– Where release management fits in an AI software factory
– Feature flags for AI agents: switching models and prompts at runtime
– Running a company in the agentic coding era, and why teams should take dumb bets

Chapters:
00:00:00 - Introduction
00:02:16 - From quarterly releases to 30,000 changes a day
00:03:32 - Autonomous rollback and automated software delivery
00:06:25 - What makes a good feature flag
00:10:46 - Defining success for coding agents
00:16:42 - Agent fleets and engineers as reviewers
00:24:05 - Feature flags in the software factory
00:29:02 - The bottleneck moves to the last mile
00:39:24 - Runtime controls for AI agents
00:44:26 - Running a company in the agentic coding era

Build your software factory, one workflow at a time, with Tessl:
https://tessl.co/e0p

🔔 Subscribe for weekly episodes on AI-native development

Where has the bottleneck moved in your team's automated software delivery? Tell us in the comments.

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.

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.

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.

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.

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.