Loop · Agentic QA · 2026
Reduce low-value testing. Apply AI where it actually compounds. Reposition QA around quality value instead of test execution.
AI-Native QE Readiness
Walk the full tiered checklist. Critical prerequisites first, then the eight readiness categories. Get a gap report your team can act on this week.
Who Loop is for
If any of these sound like the conversation in your head this quarter, you're in the right place.
“My team is shrinking, but the regression suite isn't. We're drowning in flaky tests.”
Series-B fintech · ~50 engineers
We name the 30% of your suite eating 80% of CI minutes. And the move that gets it back without breaking confidence.
See the audit brief →“Leadership keeps asking about AI. I don't yet have a defensible answer.”
Healthcare SaaS · 120 engineers
We separate AI leverage from AI theater. And give you the boss-ready memo before you sign another vendor contract.
See the entry course →“QA feels less valuable every quarter. I need to reposition the function.”
E-commerce platform · 200+ engineers
We turn QA from test execution into the quality intelligence layer your CTO will forward to the board.
See the operating-model reset →Names + companies anonymized at the speakers' request.
The bookshelf · 4 volumes
Every methodology Loop runs is published as a book. Operating model, frameworks, assessments, adoption path. Free in full, gated only by an email so we can send the bundle.
AI-Native Quality Engineering
QE-2026AI-Native Quality Engineering
Operating model for the modern QA function.
AI-Driven TDD
TDD-2026AI-Driven TDD
Tests define intent. AI accelerates implementation.
Bespoke Agentic Pipelines
AGT-2026Bespoke Agentic Pipelines
The multiplier behind every Loop methodology.
Policy as Code
POL-2026Policy as Code
The deterministic floor beneath agentic development.
Live · Self-paced
One day. $1,000/seat. Walk out with a 90-day plan you can hand to your boss on Monday.
Last Tuesday of the month · Limited seats
Read the syllabus →The same operating model packaged as on-demand tracks. First set is in production now. Drop your email on the rail and we'll send word when it lands.
4 tracks planned · Track 01 in production
See the courses landing →Watch · Latest
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
Watch on YouTube →
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
Watch on YouTube →
Apr 30, 2026
Most people trying to do agentic development are building what I call “God agents” — one giant system that tries to do everything. In my experience, that approach breaks down quickly. It hits context limits, becomes hard to reason about, and fails in inconsistent ways. The result is usually more time spent debugging the agent than actually building software. This video is about a different approach. Instead of building one massive agent, I break down the five pipeline categories that I use across my codebases to make agentic development actually work at scale. These aren’t meant to be perfect or universal, but they’ve been a solid foundation for structuring real systems. The five categories I walk through are: surface area pipelines, change type pipelines, failure mode pipelines, integration pipelines, and confidence pipelines. Each one exists for a reason. As I’ve worked more with AI, one pattern has become clear. The failures are unpredictable at first, but over time they repeat. The same classes of mistakes show up again and again. These pipeline categories are designed to target those failure patterns directly, with loops and checks that are specific to the kinds of problems you’ve already seen. The goal isn’t to create a single “correct” pipeline or a magic factory. It’s to build a system of smaller, focused pipelines that are tailored to your codebase, your architecture, and the ways AI tends to drift in your environment. If you pair this approach with something like policy as code, you end up with both structure and control, which is where agentic development starts to become much more reliable. This video is meant as a starting point. These categories will evolve, and I’m still refining them as I see new patterns emerge. If you’re building with AI and trying to move beyond simple prompts into something more scalable, this should give you a useful framework to start from. Would love to hear how others are structuring their pipelines and what categories you’re seeing in your own systems.
Watch on YouTube →How to work with Loop
A senior, AI-fluent SDET joins your team and beachheads the new operating model by living it.
Read the brief →An async-first private review of where your QA model is losing leverage. And what to do first.
Read the brief →Get QA, engineering, and product aligned on who owns quality and how release confidence gets measured.
Read the brief →6–10 weeks. Move QA from test execution to release confidence with measurable ROI.
Read the brief →Track record
30+
Engagements shipped
94%
On-time releases
−42%
Avg. regression CI minutes
0
Critical escapes (last 12 mo)
Numbers reflect engagements where Loop ran the operating-model reset or the transformation sprint. See the client roster for the full case set.
Writing
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.
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.
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.
7 min read →Resources
Drop your email and we'll send the asset. No drip funnel, no sales calendar. One email, the file, and you're done.
The exact week-by-week plan QA leaders use to defend headcount and prove output in a single quarter.
Six metrics your CTO actually cares about. Escape rate, regression drag, recovery time, leverage ratio, AI yield, ownership clarity.
Stop QA-as-bottleneck. Map every test layer to a named owner so engineering can't push everything down to your team.
12 questions. Honest score. Tells you whether your team is a cost center, a guardrail, or a leverage multiplier.
Plug in your CI minutes, retry rate, and team size. Get the dollar figure flaky tests are costing you this quarter.
How to keep release safety from collapsing when your team is shrinking and your scope is growing.
Engagement ladder
Free books and resources. Evaluate Loop's thinking with no cost.
Open the bookshelf →Public courses and workshops. Walk out with a 90-day plan.
See the workshops →A private audit of where your QA model is losing leverage.
Read the brief →Embedded SDETs, the operating-model reset, or the transformation sprint.
See all engagements →90-Day QA Leverage Plan
Coming soonUse it on Monday · Editable doc + Notion template