Artificial intelligence has picked up a lot of new vocabulary over the past couple of years, and two terms now show up in almost every tech conversation: generative AI and agentic AI.
You'll see the comparison written a few different ways: agentic AI vs generative AI, gen AI vs agentic AI, generative AI vs agentic AI. They're all pointing at the same question, what actually separates these two, and does it matter for how you work?
It's not just hype, either. McKinsey's most recent State of AI survey found that 88 percent of organizations now use AI regularly in at least one part of their business, and 62 percent are already experimenting with AI agents specifically. That's a lot of teams trying to figure out exactly what we're about to explain.
If you've used AI to draft an email or an image tool to turn a sentence into a picture, you've already used generative AI. If you've come across an AI that can plan a task, use other software on its own, and fix its own mistakes without being told to, that's agentic AI at work.
This blog breaks down the difference between generative AI and agentic AI in plain terms: what each one actually does, how they compare side by side, where you'll see them in the real world, and how a tool like ACE by HeadSpin brings a bit of both into software testing.
