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. “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.” “When you prompt AI to write a test, one agent will understand the user’s natural language commands, and another will start to execute against that plan and write actions into the test, while another agent understands what changed in the application and how the test should be healed,” said Andrew Doughty, founder and chief executivce of SpotQA, creator of Virtuoso QA. “And then if there is a failure, an agent can look into the history of that test object, and then triage it automatically and send it over to developers to investigate.” “We’ve found that customers don’t need large model-based AIs to do very specific testing tasks. You really want smaller models that have been tuned and trained to do specific tasks, with fine-grained context about the system under test to deliver consistent, meaningful results,” said Matt Young, president, Functionize Inc.