Dan Faulkner
Dan Faulkner
CEO
Dan Faulker is the CEO of SmartBear, which helps teams build, test, and ship quality software and is trusted by over 16 million developers, testers, and software engineers at 32,000+ organizations. He is passionate about helping customers create the best software possible for their industries or domains.
🎂 MoTaBirthday | April 27, 2026
I've never done a proper podcast, yet here I am.20 episodes in.Weekly cadence.Great quality conversations.Catching up with old friends.Making some new ones.100% stepping outside of my comfort zone....
4 Jun
SmartBear CEO Dan Faulkner on the quality gap created by agentic coding, why intent validation is quality's next big role, and what BearQ is trying to do about it.
3 Jun
Quality Gap
The growing disparity between the speed at which code is being produced, accelerated by AI coding tools, and the capacity of quality, security, and governance activities to keep pace with that output. As the inner loop accelerates and more software is generated faster, the outer loop becomes a bottleneck. The quality gap refers to this imbalance: more software arriving at testing and review stages than teams have the time or tools to handle thoroughly, creating pressure to either sacrifice coverage, slow down releases, or find new ways to scale quality work.
Outer Loop
The set of SDLC activities that happen after a developer's local coding work is complete, including integration, code review, quality assurance, security testing, governance, and deployment. The outer loop is contrasted with the inner loop, which covers the individual developer's fast local cycle. When AI tools accelerate the inner loop, the outer loop often becomes the bottleneck, because the teams and tools responsible for those downstream activities have not sped up to match. Quality, security, and DevOps disciplines all live primarily in the outer loop.
Knowledge Graph
A structured map of everything an application does — all its pages, pathways, workflows, and API calls — built by systematically exploring the application rather than reading its source code. In an agentic testing context, a knowledge graph is generated automatically by an agent that navigates the running application and records what it finds. It gives teams a ground-truth picture of what has actually been built, which may differ from what was specified. It can be used to inform test strategy, surface coverage gaps, and support intent validation by comparing the graph against original requirements.
Autonomy Ladder
A framework for describing levels of automation in software development, adapted from the levels of autonomy used in autonomous vehicle design. At the lowest level, all work is done manually by a person. At each higher level, more decision-making and execution is handed to tooling or agents, with the highest level representing fully autonomous operation requiring no human intervention. The framework is used to help teams understand where they currently sit, where they want to get to, and how to move between levels safely. In a testing context, the ladder might run from manual test creation, through low-code automation, through AI-assisted test generation, up to a fully agentic testing system that explores, writes, executes, and reports without human instruction.
Production Velocity
The rate at which software is successfully delivered into production, as distinct from coding velocity. A team may write code faster due to AI tools while production velocity stays the same or even drops, because quality, security, and governance activities downstream have not accelerated to match. Production velocity is the more meaningful measure of a team's actual throughput, since it reflects the full system of constraints in the SDLC rather than just the speed of the coding step.
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Coding velocity is not delivery velocity. We explore why.
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