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

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

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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:
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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:
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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:
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Where is your organization on the path from copilot to AI coding agents? Tell us in the comments.

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

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Which agent feedback loop would you build first in your own software factory? Tell us in the comments.