Traditional test metrics like automation %, pass/fail rates, and defect counts don’t reflect the impact of introducing agents into the QA process. This blog explores a new class of KPIs designed to measure how well your virtual test team is performing including Agent Assist Rate, Human Override Rate, Scenario Coverage Delta, and Review Time Saved. These metrics focus on insight, collaboration, and confidence, not just execution speed helping QA leaders understand where agents are truly adding value, and how to scale them responsibly.
As more organizations begin experimenting with AI-augmented QA, the focus often starts with tooling: agents that summarize logs, draft test cases, or identify gaps. But adopting these tools without rethinking your measurement model is like upgrading the engine but keeping the speedometer from a bicycle.
In this blog, we explore the next generation of QA metrics not for evaluating the systems under test, but for understanding the impact, reliability, and maturity of your agent-augmented test team.
The future of testing performance isn't just “how fast” or “how many tests.” It’s:
How intelligently are we identifying risk and how confidently can we trust the agents helping us do it?
Most testing orgs still track KPIs like:
These metrics aren’t wrong but they’re incomplete in an agent-augmented model, because they:
Here’s what we should start tracking as we introduce agents into the QA lifecycle even in traditional software environments:
What it is:
The % of test cases, triage events, or summaries where an agent was used to accelerate or assist human decision-making.
Why it matters:
What it is:
How often agent suggestions (e.g., scenario drafts, priority tags) are corrected or rejected by humans.
Why it matters:
What it is:
The % of production or test session behavior not currently represented in existing test scenarios - as identified by an agent.
Why it matters:
🔌 Tools like Katalon TrueTest already enable this kind of visibility by capturing manual test flows and turning them into reusable test assets creating a baseline for agentic coverage tracking.
What it is:
Tracks time saved when humans review and finalize agent-generated content compared to manual authoring from scratch.
Why it matters:
What it is:
Why it matters:
| If you're asking... | These metrics help answer... |
|---|---|
| Are agents actually helping us? | Agent Assist Rate, Review Time Saved |
| Can we trust what they generate? | Human Override Rate |
| Are we testing the right things? | Scenario Coverage Delta |
| Is our test suite stable? | Reuse vs. Drift Rate |
| Where should we scale next? | Agent adoption patterns + feedback loops |
Even if you’re early in your journey, you can start building the telemetry and structure to support this:
This will set the foundation for governed, explainable agentic QA at scale and enable you to demonstrate value with data.
Legacy metrics were built for script authors and regression runners.
The new testing stack includes test architects, augmentation agents, and collaborative workflows. If we keep measuring the old way, we’ll miss the biggest shift of all:
The move from testing as execution, to testing as intelligence.
Blog 9: Agentic QA as a Quality Operating Model
We’ll step back from individual agent roles and look at how a virtual QA team could operate as part of your broader delivery process from governance to release readiness to defect prevention.