SiliconANGLE article by Jason English
Agentic artificial intelligence is the new belle of the software ball. C-level executives want their companies to use AI agents to move faster, therefore driving vendors to deliver AI agent-driven software, and every software delivery team is looking for ways to add agentic capabilities and automation to their development platforms.
By parallel coding with co-pilots, some pundits are speculating that developers could increase their code output by 10 times. But how good is that output, and does AI-generated code increase the test coverage requirements beyond the reach of humans?
Despite quality concerns and developer misgivings, there’s simply too much potential value in AI development and testing tools that can do work quickly and semi-autonomously to put the toothpaste back in the tube. We’ll eventually have to test AI agents with AI agents.
It’s no wonder a recent survey found that two-thirds of companies are either already using or planning to use multiple AI agents to test software, and that 72% believe agentic AI could test software autonomously by 2027.
Where do you start with agent-based testing?
Newer companies have the advantage of working with AI from the start, seemingly inheriting less technical debt from hand-rolled applications and tests. While startup teams can move much faster, at the same time, they may not have enough implementation experience to understand where to look for errors.
Bringing AI testing agents into the team can help, but once they are tasked with finding bugs, they may generate far more test feedback than expected. Now developers find themselves trying to separate genuine errors from false positives, which definitely cools the vibe in vibecoding.
“The only purpose of adopting agents is productivity, and the unlock for that is verifiability,” said David Colwell, vice president of artificial intelligence, Tricentis, an agentic AI-driven testing platform. “The best AI agent is not the one that can do the work the fastest. The best AI agent is the one that can prove that the work was done correctly the fastest.”
In a sense, established enterprises with long-running DevOps tool chains do have one advantage over nimbler startups: being able to roll existing requirements, documentation, customer journeys, architectural diagrams, procedures, test plans, test cases and even robotic process automation bots into a corpus of AI contextual knowledge, which can provide foundational skills for informing a swarm of specialized test agents…
Read the whole article at SiliconANGLE here: https://siliconangle.com/2025/10/10/test-agentic-ai-apply-agents-liberally/
