What Reflection AI does
Reflection AI is a private AI lab focused on building frontier “open” intelligence—models intended to be distributed as open weights and paired with open-source tooling—aimed at enabling organizations to run and customize powerful agentic systems on their own infrastructure. The company’s stated thesis is that autonomous coding is the “root node” problem for broader superintelligent computer work, and its research program sits at the intersection of reinforcement learning and language models.
From a product standpoint, Reflection has shipped its first named offering, Asimov, described as a code comprehension “research agent” for engineering teams. Sequoia describes Asimov as ingesting codebases and engineering knowledge sources, building a persistent memory layer, and using a multi-agent architecture (retriever agents plus a reasoning “combiner”) to synthesize answers about large codebases.
Strategically, Reflection has moved beyond an initial autonomous-coding framing to a broader “open intelligence” positioning. In an October 9, 2025 product/news post, Reflection says it has assembled an LLM + reinforcement learning training stack capable of training frontier Mixture-of-Experts (MoE) models at scale and that it has raised $2 billion, framing the goal as scaling open weights and the surrounding stack (software, infrastructure, and solutions) for developers, enterprises, and public sector organizations.