Past the Bottleneck
A field guide to quality in the AI-SDLC. AI made shipping code faster. Now verification is the bottleneck.
Inside this ebook
You'll learn how high-performing teams are rethinking QA.
- Why AI coding tools shifted the bottleneck from development to verification
- What “agentic testing” actually means (and what it doesn’t)
- How autonomous QA agents work inside modern CI/CD workflows
- Why traditional scripted automation breaks at AI-era velocity
- What a practical rollout looks like – from pilot to full adoption
- The KPIs that actually matter when evaluating AI testing platforms
- How leading teams are building continuous quality systems, not just test suites
Written by
Daniel Mauno Pettersson
CEO
Tech leader and hands-on engineer for the last 20 years. Previously CTO at memmo, Billogram, Dooer, CEO at Agigen.
LinkedIn
Patrick Lef
CPO
Engineer-turned product leader, detail-oriented. Previously CPO/CTO, memmo, Collabs, Besedo, Videofy.
LinkedIn
Vilhelm von Ehrenheim
CAIO
Built EQT's Motherbrain from nothing to a market leader. Lead Data Scientist at Klarna.
LinkedIn
Table of contents
Six chapters. One coherent picture of quality in the AI-SDLC.
- —
Foreword
Why we wrote this guide, and what changed in 2025 that made it necessary.
- 01
The bottleneck that moved
Why verification became the new constraint in modern software delivery – and why legacy QA processes collapse under AI-driven development velocity.
- 02
What agentic testing actually is
A deep dive into perception–action loops, browser agents, knowledge graphs, and adaptive testing systems that verify user intent instead of replaying scripts.
- 03
How it works in your dev loop
How AI testing agents integrate into pull requests, staging environments, exploratory testing workflows, and regression suites.
- 04
Adoption playbook
A rollout framework used across dozens of engineering teams – from initial proof-of-concept to organization-wide deployment, including the KPIs that actually matter when measuring impact.
- 05
What teams are actually seeing
Faster regression cycles, reduced QA overhead, bugs caught before production, less flaky automation, and higher release confidence.
- 06
The path ahead
Where AI-powered quality systems are heading next – from PR-level guardrails to fully adaptive product excellence engines.
FAQ
Questions teams ask before reading.
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