Brokerage platforms could start to see a rise in clients running parallel AI agent strategies, with Alpaca becoming one of the first few to record almost a six-fold increase in individual users. It is observing a similar surge in API activity as denoted by data based on the past 18 months.
Alpaca is a US-based financial technology company that provides brokerage infrastructure and trading APIs. During Quantinsti's 16th anniversary conference, it shared data to shows a six-fold rise in users running parallel AI agent strategies over 18 months. Simple buy-and-hold mandates are increasingly shifting to LLM-orchestrated systems, driving demand for multi-account, API-first infrastructure, while complex and judgment-heavy portfolios still require human oversight.
Clients who previously maintained one or two paper-trading or live accounts are now routinely running dozens (in some cases close to a hundred) of concurrent agent-driven strategies. Each agent tests a different approach, from simple index replication to more active rules-based systems.
The same platforms are also seeing a new category of user: long-term, buy-and-hold investors. These clients had earlier paid traditional advisers 1 to 2 percent a year for portfolios that largely tracked broad market indices. With the ability to prompt language models and execute through APIs, many are now simulating and running those same strategies themselves at far lower cost.
The demand has the potential to push brokers to prioritise multi-account infrastructure, robust API access and agent-friendly tooling. At the same time, speakers on the panel stressed that complex or judgement-heavy mandates still require human oversight: concentrated equity books, private-market exposure, or portfolios with significant regulatory and risk considerations. Soft skills such as understanding client context, regulatory intent and edge-case risk remain difficult to automate.