The AI agent market is crowded and commoditized
There are dozens of tools for building AI agents, with little to distinguish one from another
The market for AI agents is very crowded and increasingly commoditized. I asked You.com, the AI-powered search engine, for a list of AI agent builder tools. You can see its answer here. There are a lot of these tools available—and I bet that You.com didn’t capture all of them. This presents a real problem for people trying to build in the space. Following is a discussion of some of the issues that these companies will need to deal with.
Differentiation and staying ahead of the curve
AI is a crowded and fast-moving space. It is difficult for any one company to differentiate its product from its competitors’ and also stay ahead of the competition. AI capabilities are rapidly advancing, so what seems cutting-edge today may be table stakes tomorrow. If OpenAI includes AI agent functionality in GPT5, many extant AI agent tools will die. Sam Altman has hinted as much about GPT5. What use do customers have for an AI agent tool if foundational models suddenly have the ability to build agents natively?
Adapting to changing customer needs
Customers’ expectations and needs will change as AI technology evolves. What customers want from an AI agent building tool today may be quite different next year or the year after that. Companies need to stay close to their customers, understand their pain points, and adapt their product roadmaps accordingly. As I wrote yesterday, many of these AI startups have adopted a horizontal customer acquisition strategy. It is much harder to stay close to your customers when your customers are in a variety of different industries. It’s much easier to maintain close relationships with your customers when they all operate in the same industry. These startups need to be agile enough to pivot if market demands shift. It’s hard to be agile when your customer acquisition strategy is a diffuse, horizontal one.
Overcoming barriers to adoption
Even though interest in AI remains high, the customers of these AI agent building platforms struggle to implement and use AI tools. Challenges include lack of quality training data, need for new infrastructure and job roles to support AI, and ethical concerns. AI agent tool providers need to help customers overcome these barriers by providing not just the core technology, but the supporting data, explainability, and change management to drive successful adoption.
However, all of this increases customer acquisition cost. Since there are so many AI agent tools out there, it’s a commoditized market, where prices are in a race to the bottom. That’s not an enviable position for any company to be in, let alone a thinly capitalized tech startup.
Balancing ambition and pragmatism
It’s tempting for entrepreneurs to chase ambitious, moonshot AI projects, but these often encounter setbacks or outright failure. One way to avoid this is to take an incremental approach, in which a company starts with narrower AI applications and builds up capabilities over time. Successful entrepreneurs navigate the balance between ambition and pragmatism. One possible strategy for companies buiding AI agents is to specialize in AI agents for a particular industry. Get close to the customers in that industry, learn their pain points, and build AI agents which solve those specific pain points.
Where does this leave AI agent startups?
The ones which will thrive will become experts in one particular industry, and solve problems for that industry’s companies. They will become recognized experts in the application of AI to a specific industry. Once they have a sufficient number of customers, they can expand to adjacent industries. But those AI agent companies that try to be everyone’s friend, and source customers from any and all industries, will find that they can’t compete in a commoditized market. And they will find it even harder to sustain their operations if foundational models eventually include native agent creation functionality.