According to a Gartner forecast, an average Fortune 500 company is expected to increase its number of artificial intelligence agents from fewer than 15 in 2025 to over 150,000 in 2028. Analysts have dubbed this phenomenon “agent sprawl,” referring to the uncontrolled expansion of these agents within organizations. Only 13% of companies currently claim to have adequate governance processes in place to manage them.
These agents, capable of performing tasks autonomously, are beginning to spread across areas such as customer service, product development, and operations. As they gain autonomy, a new challenge arises: ensuring that everyone understands the customer from the same context.
Currently, this does not always happen. Each agent may consult a different source, and the understanding of the customer ends up being rebuilt from scratch with each new demand. Without a common layer of context, the work is repeated—and different agents may arrive at different answers or decisions for the same customer.
Birdie’s proposal
This is the environment in which Birdie operates—a startup founded by Brazilians in Silicon Valley. The company converts the various signals generated by customers into a shared context, enabling people and AI agents to make decisions based on the same understanding. The platform consolidates information from conversations, surveys, complaints, usage data, customer segmentation, and other interactions into a single repository.
“If ten agents need to understand the same customer before performing ten different tasks, how much of that understanding really needs to be rebuilt ten times?” asks Alexandre Hadade, CEO and co-founder of the company. “The problem is not just the cost of redoing it. It’s the risk of ten different answers for the same customer.”
When AI agents start participating in decisions, the lack of a unified view becomes even more critical. If a customer cancels a purchase or service, the product, customer experience, and support teams may come up with different explanations for the same case, each based on a system that does not communicate with the others.
Birdie calls this phenomenon a lack of context governance. The solution proposed by the startup is the creation of a database of information that can be consumed by different systems, with a record of the origin of the data and how it was used.
“The more autonomy we give to AI, the more important the quality of the context behind it becomes,” says Hadade.
From analysis to decision
The use of this context layer is already observed in companies that deal with large volumes of customer interactions.
At the Canadian financial services company KOHO, Birdie’s platform began analyzing a larger portion of consumer interactions. According to the startup, this helped reduce by 80% the structure dedicated to evaluating customer service and training employees. The estimated savings are about US$ 350,000 per year.
At the Brazilian fintech Neon, analysis of interactions identified problems that were evolving into regulatory complaints. Based on these signals, the company adjusted procedures still in customer service. In two quarters, according to Birdie, the index of regulatory complaints fell by 44%.
These examples illustrate how information generated in customer service can be used outside of it. A complaint may indicate a product problem. A conversation may point to a process failure. A change in satisfaction may signal an issue that has not yet appeared in traditional indicators.
As the number of agents operating in companies increases, this information begins to feed a larger number of systems. The discussion, therefore, is not just about how intelligent each agent is, but about the quality of the context it will have to make decisions.
“We don’t believe companies should stop building their agents. On the contrary: they will build more and more,” says Hadade. “But our vision is that when they create the next agent, it doesn’t have to start from scratch to understand the customer.”
