The Loop Dispatch
Issue 006 · March 14, 2026
Lead essay · Quality Engineering
Most QA functions were built for a world where humans wrote all the code. That world is gone. Here's the operating model we've deployed across 30+ engagements to replace it.
Most QA functions were built for a world where humans wrote all the code. That world is gone. Here's the operating model we've deployed across 30+ engagements to replace it.
About the Author
Ben Fellows
The fundamental problem with traditional QA isn't headcount, tooling, or process. It's the operating model itself. QA was designed as a gate…
All Articles
Most QA functions were built for a world where humans wrote all the code. That world is gone. Here's the operating model we've deployed across 30+ engagements to replace it.
Ben Fellows
8 min read
Everyone is shipping AI agents with giant prompts. The teams getting real leverage are building purpose-built pipelines with roles, permissions, and project-specific rules instead.
Marcus Rivera
6 min read
We redesigned the TDD workflow for AI-assisted development. Tests define intent, AI accelerates implementation, and engineers own verification. The results changed how we think about quality ownership.
Sarah Chen
7 min read
Velocity metrics tell you how fast you're moving. Leverage metrics tell you whether the speed is sustainable. Here's the measurement framework we use with every client.
Ben Fellows
5 min read
Most maturity models are academic exercises. This one was built from 30+ real assessments and tells you exactly what to fix first based on where the leverage is highest.
Priya Sharma
6 min read
The SDET role is being redefined. Manual test execution is gone. What remains is automation engineering, infrastructure design, and the ability to operate AI-assisted workflows. Here's how we hire for it.
James Okafor
5 min read
How readers use the writing
The essays are most useful when they give you language and evidence for an argument you already wanted to make.
“I sent “Coverage Is a Vanity Metric” to my CTO. Two weeks later we redefined our QA OKRs.”
Series-B fintech · ~50 engineers
Used: Coverage Is a Vanity Metric
“The AI-generated tests piece reframed our vendor review. We rewrote the evaluation rubric the same week.”
Healthcare SaaS · 120 engineers
Used: Why Your AI-Generated Tests Are Worthless
“I forwarded the staffing-model essay to our head of engineering. It started a conversation we'd been avoiding.”
E-commerce platform · 200+ engineers
Used: The Staffing Model Is Broken
Names + companies anonymized at the speakers' request.
Watch · From the desk
May 1, 2026
Are agentic pipelines actually worth the extra time, tokens, and complexity? My honest answer: it depends. Agentic pipelines can improve accuracy, visibility, governance, and control, but they also add real cost. They often take longer to run, use more tokens, introduce more orchestration, and create another layer of abstraction around your development process. So the question is not “do pipelines work?” The better question is: did this pipeline earn its cost? In this video, I walk through the framework I’m using to evaluate whether an agentic pipeline is actually worth running. That includes measuring the pipeline tax, tracking run receipts, comparing quality improvements, and using a ledger system to understand whether a pipeline is making the work better or just making it more complicated. I also share an example of a pipeline that looked good on paper but probably wasn’t worth it in practice. That’s an important part of the lesson: not every task needs a pipeline. Sometimes a single Claude Code or Codex session, guided by a strong engineer, is enough. The goal is to use pipelines surgically. Start simple. Measure what happens. Add complexity only when the pipeline is solving a real problem. And when a pipeline gets too large, use the data to make it smaller. If you’re experimenting with agentic development, this video is about how to think about ROI, accuracy, governance, and cost before building complex AI workflows everywhere.
Apr 30, 2026
Stop treating AI like factory work. Rigid, assembly-line workflows break down with complex codebases. Owning your agentic pipeline means customizing every step and refreshing context each time—leading to better accuracy, flexibility, and scalability where it actually matters. #agenticai #agentic #agenticcoding #coding
Apr 30, 2026
Why “agentic factories” don’t actually work Trying to force one system to handle every codebase is like expecting one factory to build every type of car - it just doesn’t scale. Instead, the future is agentic pipelines: flexible, tailored workflows built around your specific repo, while still reusing powerful components like agents, prompts, and shared memory. Smarter systems aren’t universal - they’re purpose-built. #agenticai #agentic #agenticcoding #coding #programming #code
Every article is backed by a published methodology paper. Download the frameworks, run the assessments, and see the full system.
90-Day QA Leverage Plan
Coming soonUse it on Monday · Editable doc + Notion template