Software testing has spent decades becoming more automated. The next shift is different.
AI agents are beginning to do more than execute predefined test cases. They can interpret requirements, generate tests, choose testing paths, analyse failures and adapt to changes in an application.
That creates a new question for enterprises: when an AI agent starts making testing decisions, who validates those decisions?
The latest World Quality Report 2025–26 from Capgemini, Sogeti and OpenText found that 89% of organisations are already piloting or deploying GenAI-augmented Quality Engineering workflows. Yet only 15% have reached enterprise-wide implementation.
The gap is revealing. Organisations are moving quickly to experiment with AI in quality engineering, but scaling it responsibly is proving considerably harder. The reason is simple: AI doesn’t just automate testing. It changes how testing works.
Traditional automation largely followed a predictable model.
Engineers defined a scenario, specified an expected outcome and created an automated sequence to validate it. When the test failed, teams could investigate the predefined steps and identify where the application behaved differently from expectations.
Agentic testing introduces another layer of decision-making.
An agent may decide which scenarios deserve attention, generate additional tests based on what it observes, interpret failures and alter its approach as the application changes. That flexibility can make testing dramatically more adaptive.
It also creates a new risk: a test can pass while the testing decision itself was wrong.
An agent might misunderstand a requirement, work with incomplete data or modify a test in a way that makes it easier to pass without preserving its original intent. That’s why the industry’s conversation around AI in testing needs to move beyond productivity.
AI can accelerate a mature quality process but it can also accelerate a weak one. If requirements are ambiguous, test data is unreliable or environments are unstable, AI doesn’t automatically fix those foundations. It can expose the same weaknesses at a greater scale.
For years, organisations have measured testing automation through metrics such as execution time, test coverage and the number of tests automated.
In an agentic environment, the more important question becomes: Can we trust the decisions being made by the system that is testing our software?
That requires a different approach to autonomy.
AI agents need clearly defined boundaries around what they can access and what actions they can take. The ability to generate or modify a test should not automatically mean the ability to approve a release.
High-impact decisions should remain auditable, and organisations should be able to understand why an agent reached a particular conclusion.
Human involvement also needs to evolve. This doesn’t mean putting engineers back into the loop to manually review every action an AI agent takes. That would simply recreate the bottleneck that automation was designed to remove.
Instead, human expertise needs to move higher up the chain: defining intent, identifying business risk, setting boundaries and challenging decisions when the evidence does not look right. This is particularly important as AI becomes part of the applications being tested themselves.
Software is becoming increasingly capable of interpreting context, responding to users and making decisions rather than simply following deterministic instructions. Testing therefore has to evaluate behaviour and outcomes, not just whether a predefined sequence produced an expected result.
The opportunity is significant.
AI agents can help testing teams explore more scenarios, respond to changes faster and reduce the repetitive work that consumes valuable engineering time. But autonomy should not be mistaken for trust.
The organisations that benefit most from agentic testing will not necessarily be those that give AI the greatest control. They will be those that build the strongest systems around that control.
The next generation of software quality will therefore require a different balance: AI for scale and adaptation; humans for intent, accountability and risk.
Authored by Harry Rao, Founder & CEO of TestGrid