A new kind of artificial intelligence agent, trained to understand how software is built by gorging on a company’s data and learning how this leads to an end product, could be both a more capable software assistant and a small step towards much smarter AI.
The new agent, called Asimov, was developed by Reflection, a small but ambitious startup confounded by top AI researchers from Google. Asimov reads code as well as emails, Slack messages, project updates and other documentation with the goal of learning how all this leads together to produce a finished piece of software.
Reflection’s ultimate goal is building superintelligent AI—something that other leading AI labs say they are working towards. Meta recently created a new Superintelligence Lab, promising huge sums to researchers interested in joining its new effort.
I visited Reflection’s headquarters in the Brooklyn neighborhood of Williamsburg, New York, just across the road from a swanky-looking pickleball club, to see how Reflection plans to reach superintelligence ahead of the competition.
The company’s CEO, Misha Laskin, says the ideal way to build supersmart AI agents is to have them truly master coding, since this is the simplest, most natural way for them to interact with the world. While other companies are building agents that use human user interfaces and browse the web, Laskin, who previously worked on Gemini and agents at Google DeepMind, says this hardly comes naturally to a large language model. Laskin adds that teaching AI to make sense of software development will also produce much more useful coding assistants.
Laskin says Asimov is designed to spend more time reading code rather than writing it. “Everyone is really focusing on code generation,” he told me. “But how to make agents useful in a team setting is really not solved. We are in kind of this semi-autonomous phase where agents are just starting to work.”
Asimov actually consists of several smaller agents inside a trench coat. The agents all work together to understand code and answer users’ queries about it. The smaller agents retrieve information, and one larger reasoning agent synthesizes this information into a coherent answer to a query.
Reflection claims that Asimov already is perceived to outperform some leading AI tools by some measures. In a survey conducted by Reflection, the company found that developers working on large open source projects who asked questions preferred answers from Asimov 82 percent of the time compared to 63 percent for Anthropic’s Claude Code running its model Sonnet 4.
Daniel Jackson, a computer scientist at Massachusetts Institute of Technology, says Reflection’s approach seems promising given the broader scope of its information gathering. Jackson adds, however, that the benefits of the approach remain to be seen, and the company’s survey is not enough to convince him of broad benefits. He notes that the approach could also increase computation costs and potentially create new security issues. “It would be reading all these private messages,” he says.
Reflection says the multiagent approach mitigates computation costs and that it makes use of a secure environment that provides more security than some conventional SaaS tools.
