EXCLUSIVE: Subconscious is tackling one of the biggest challenges facing long-running AI agents: inference cost.
@subsysdev has raised $5.1 million to build an inference platform designed specifically for agents, using dynamic context compression and caching to make long-running workloads faster and cheaper. The company says its technology can reduce inference costs by up to 80%, while extending effective context windows beyond 5 million tokens. For businesses scaling agentic AI, that could make it more practical to run agents for longer periods on increasingly complex tasks.
@deantak spoke to
@thejackobrien, CEO of Subconscious, about the company’s technology, its focus on long-running agents and the economics of powering the next generation of AI applications.
“Most inference companies build one-size-fits-all systems for chats, one-shot requests, and agents alike. But agents are much harder to serve well. We built our whole stack around long-running agents, and our core technology comes from novel MIT research, which gives us a year-plus head start,” O’Brien said.
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