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Continuous Delivery 2.0
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Continuous Delivery 2.0

Tech and case studies on organizational management, team management, and engineering efficiency

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Recent Articles

Latest from Continuous Delivery 2.0

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Continuous Delivery 2.0
Continuous Delivery 2.0
Sep 23, 2026 · Industry Insights

QA-to-Dev Shifts: Why Title Removal ≠ Testing Duty Loss — Microsoft's 2014 SDET Lesson

Meituan and ByteDance pilot QA-to-developer transitions, but Microsoft's 2014 SDET elimination shows title removal doesn't erase testing duties; the article distinguishes role boundaries from quality responsibilities, noting AI speeds test generation but not quality judgment, and urges evaluating actual responsibility shifts over title changes.

AI in testingGoogleMicrosoft
0 likes · 11 min read
QA-to-Dev Shifts: Why Title Removal ≠ Testing Duty Loss — Microsoft's 2014 SDET Lesson
Continuous Delivery 2.0
Continuous Delivery 2.0
Sep 10, 2026 · R&D Management

AI Agent Era: CI/CD Isn't Dead—It's Now Essential Infrastructure

This article argues that CI/CD hasn't been replaced by AI but has become indispensable infrastructure like utilities, explaining why stronger AI demands stronger organizational control over quality gates, automated testing, rollback capabilities, and knowledge assetization, distinguishing personal empowerment from organizational governance.

AI AgentCI/CDContinuous Delivery
0 likes · 8 min read
AI Agent Era: CI/CD Isn't Dead—It's Now Essential Infrastructure
Continuous Delivery 2.0
Continuous Delivery 2.0
Sep 9, 2026 · Artificial Intelligence

Matt Pocock's Agent Skill Design Checklist: 4 Steps to Reliable Skills

This article summarizes Matt Pocock's four-step checklist for designing effective AI agent skills: choose between user-invoked and model-invoked triggers, structure skills with steps and references using context pointers, control agent behavior with concise guide words like 'vertical slice', and prune redundancy by eliminating duplication, sediment, and no-ops.

AI Agent DesignAgent SkillsContext Pointers
0 likes · 9 min read
Matt Pocock's Agent Skill Design Checklist: 4 Steps to Reliable Skills
Continuous Delivery 2.0
Continuous Delivery 2.0
Sep 9, 2026 · Artificial Intelligence

Meta's JiTTesting: Disposable Test Probes Catch AI-Generated Code Defects

Meta's JiTTesting generates temporary, diff-specific test probes that run on both parent and new code versions to catch behavioral differences introduced by AI-generated changes, using dual pipelines (Dodgy Diff and Intent-Aware), noise reduction via RubFake and LLM-as-Judge, and human-in-the-loop review, while promoting stable passing tests to the permanent hardening suite.

AI-generated codeCI/CDJiTTesting
0 likes · 11 min read
Meta's JiTTesting: Disposable Test Probes Catch AI-Generated Code Defects
Continuous Delivery 2.0
Continuous Delivery 2.0
Sep 8, 2026 · Fundamentals

JiTTesting: Just-in-Time Testing for AI-Speed Code Changes

JiTTesting introduces a dual-track testing strategy—permanent hardening tests for regression prevention and temporary catching tests generated on-demand to detect behavioral differences in AI-generated code diffs—using parallel pipelines, automated noise reduction, and human-in-the-loop validation to keep pace with rapid AI-driven development.

AI-assisted testingJiTTestingLLM-based testing
0 likes · 10 min read
JiTTesting: Just-in-Time Testing for AI-Speed Code Changes
Continuous Delivery 2.0
Continuous Delivery 2.0
Sep 7, 2026 · Artificial Intelligence

HITL Isn't a Popup: 5 Risk-Tiered Rules to Govern AI Agents

This article explains that Human-in-the-Loop (HITL) for AI agents is not merely a confirmation dialog but a risk-tiered governance mechanism, presenting five practical rules: risk classification, clear context for human decisions, audit logging, default deny on timeout, and feedback loops for continuous improvement.

AI AgentsAI governanceAI safety
0 likes · 10 min read
HITL Isn't a Popup: 5 Risk-Tiered Rules to Govern AI Agents
Continuous Delivery 2.0
Continuous Delivery 2.0
Sep 7, 2026 · Artificial Intelligence

Enterprise AI Engineering 2.0: From Prompt Crafting to Constrained Runtime Environments

The article argues that enterprise AI engineering is moving from fragile prompt-centric specifications to robust environment-driven verification, detailing four essential capabilities—automated validation loops, tool-call fault tolerance, hard permission isolation, and asset lifecycle management—to build governable, self-correcting AI runtime environments.

AI engineeringSpec-Driven DevelopmentTool Calling
0 likes · 9 min read
Enterprise AI Engineering 2.0: From Prompt Crafting to Constrained Runtime Environments
Continuous Delivery 2.0
Continuous Delivery 2.0
Sep 5, 2026 · Artificial Intelligence

HITL Isn't a Popup: 5 Rules for Human-in-the-Loop AI Safety

This article clarifies that Human-in-the-Loop (HITL) is not merely a confirmation dialog but a systematic safety framework for AI agents, detailing five production rules, three common misconceptions, and two real-world scenarios to distinguish HITL from HOTL and HOOTL.

AI AgentsAI safetyAudit Logging
0 likes · 7 min read
HITL Isn't a Popup: 5 Rules for Human-in-the-Loop AI Safety