The tech world is abuzz with talk of AI agents. Everyone seems to be building them, from startups to tech giants. But there's a crucial element that's often overlooked: experience.
When we think about what makes humans valuable in the workplace, it's not just their raw intelligence or skills. It's their accumulated experience. A seasoned salesperson knows which leads are worth pursuing because they've spent years building relationships and learning the nuances of their industry. An experienced engineer instinctively knows which technology stack will work best for a given problem because they've built similar systems before.
This experience is what's missing from most AI agents today.
Sure, we can train an agent on vast amounts of data, give it access to all sorts of information, and even imbue it with impressive reasoning capabilities. But that's not the same as experience. Experience is contextual, cumulative, and often tacit. It's the kind of knowledge you can't just download.
Consider a human employee. When they join a company, they don't just bring their skills and knowledge. They bring their network, their industry insights, their understanding of unwritten rules and best practices. More importantly, they continue to grow. They learn from colleagues, adapt to the company's culture, and accumulate valuable context-specific knowledge over time.
Now think about an AI agent. In most current implementations, it starts from scratch with each new user or company. It doesn't have a network. It doesn't have years of industry experience. It doesn't understand the subtle dynamics of your specific business.
This is where the next big opportunity lies.
The startups that will win in the age of AI agents won't just be building smarter algorithms. They'll be building systems that can accumulate and leverage experience in meaningful ways. They'll create agents that can learn not just from data, but from interactions, from successes and failures, from the specific context of each business they serve.
Imagine an AI sales agent that doesn't just know how to write a good email, but actually builds and maintains relationships over time. Or a coding assistant that doesn't just know programming languages, but understands your company's codebase, your team's coding style, and the history of your product decisions.
This kind of experiential learning is what will separate truly valuable AI agents from glorified chatbots. It's what will create real, defensible moats in a world where raw AI capabilities are becoming increasingly commoditized.
Building this kind of system is hard. It requires solving tricky problems around data privacy, continuous learning, and context management. But the startups that crack this nut will have something incredibly valuable: AI agents that get better over time, that build up institutional knowledge, that become more valuable the longer they're used.
In a sense, we need to make our AI agents more like our best employees: capable of learning, growing, and accumulating valuable experience over time. The future of AI isn't just about making agents smarter; it's about making them more experienced.
The path to truly useful AI agents isn't just about better models or more data. It's about bridging the gap between raw intelligence and accumulated wisdom. It's about building AI that doesn't just know things, but understands them in context. In other words, it's about giving our AI agents what every great employee has: experience.