Perplexity's "Search as Code" lets AI models write their own search pipelines instead of calling fixed APIs

Instead of calling a ready-made search API, models in Perplexity's new "Search as Code" architecture write their own search workflows as Python code. The company promises more precise results and lower token usage.

Anyone who's watched an AI agent tackle a complex research task has seen the same pattern. The model writes a query, a search API returns a list of results, the model reads them, and then writes the next query. This loop repeats, often many times in a row.

Perplexity calls this a bottleneck in a new technical report. Today's search engines were built for humans who want a neat list of blue links, but for an AI agent trying to run hundreds of searches in a few minutes, that setup is too rigid. The agent can only tweak the search term; everything else is a black box.