By 2027, 70% of professional developers are expected to be using AI-powered coding tools, up from less than 10% in September 2023, according to Gartner Research. And within three years, 80% of enterprises will have integrated AI-augmented testing tools into their software engineering tool chain — a significant increase from approximately 15% early last year, Gartner said.
Beyond coding, AI agents are designed to perceive their surroundings, make decisions based on that information, take actions, and sometimes learn and adapt over time to perform tasks autonomously. Reinforcement learning is key to agentic AI’s ability to continue to grow in sophistication when performing tasks.
“If you’re playing a game, you either win or lose. If you lose, you go back and evaluate why, and then play again but do it differently,” Kapoor said. “With agentic AI, there are a very defined set of KPIs that you’re asking it to meet, so it would know whether it has met them or not. And then it goes back and it reinforces itself to do this task differently.”
This story originally appeared on Computerworld
