This article has an interesting take on agentic AI vs generative AI and does a great job of describing how they’re different and more!
The article from TechRadar Pro (Agentic AI vs generative AI: why the future’s not just smarter—it’s bolder, written by Rehan Refai and available here), uses a great set of analogies to differentiate the two, as follows:
Generative AI: The genius with no to-do list
Businesses adore Generative AI for its ability to complete routine tasks. Whether summarizing documents or creating social media visuals, it’s already transforming industries, with McKinsey reporting that 71% of organizations use it in at least one business function. Early adopters are already seeing impressive returns, delivering an average of $3.70 in value for every dollar invested.
You’ve undoubtedly seen generative AI in action—chatbots that write like Hemingway, image tools that can paint a Studio Ghibli cat playing chess with Einstein, the tools that code. It’s impressive. But here’s the catch—it’s passive.
While both Generative and Agentic AI spring from similar foundational technologies, their applications diverge significantly. Simply put, Generative AI doesn’t initiate. It reacts. You ask, it answers. You prompt, it paints. You guide, it follows. And for a while, that was enough. We built content generators, piloted promising tools, and deployed internal copilots for knowledge management. But now the question is shifting—from “how smart is the output?” to “what actions can it take?”
Meet Agentic AI: The self-starter with an agenda
Agentic AI isn’t just smart. It’s assertive. These systems aren’t just responding—they’re deciding. They’re setting goals, making plans, and executing them, all (mostly) without your nudging.
If Generative AI is like a talented artist creating stunning works on command, think of agentic AI as a highly competent chief of staff. You give it a direction—“improve customer churn”—and it starts to act. It looks at retention data, cross-checks CRM logs, generates hypotheses, triggers outreach campaigns, and, crucially, updates its approach as new data rolls in. All while you’re in a different meeting entirely. Agentic AI uses reasoning, decision-making algorithms, and environment-based data to act and adapt.
What truly sets Agentic AI apart is its ability to harness the distributed nature of knowledge and expertise. Traditional AI often operates within fixed boundaries, following predetermined paths. Agentic systems can break down complex tasks into smaller, manageable sub-tasks, identify the right specialized agents for these sub-tasks, then orchestrate interactions between agents to synthesize solutions efficiently.
The article goes into more depth, but you get the idea.
Agentic AI seems tailor made for eDiscovery – after all, we’re used to developing automated workflows with agents performing tasks and we’ve been doing it without applying AI. While the risks of doing so with AI are perhaps increased, the potential rewards in terms of what you can accomplish with agentic AI are even greater. Here’s one example of that.
So, what do you think? Do you feel like you understand the difference between agentic AI vs generative AI? Please share any comments you might have or if you’d like to know more about a particular topic.
Image created using GPT-4’s Image Creator Powered by DALL-E, using the term “robots reviewing documents on computers”.
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