GoKawiilSearch is a core primitive for AI systems. Frontier models grow more capable by the month, but they still need access to fresh, accurate, and well-curated knowledge from the wider world. Search is the primary way that AI systems tap into that knowledge, and thus a foundational component of any product that needs to draw conclusions, take actions, and perform real-world work.
We believe that traditional search pipelines are increasingly outdated in the era of agents. Traditional search answers queries, but today’s agents complete tasks that can take on countless shapes. These tasks require agents to define task-specific retrieval strategies directly within their harnesses. Within Perplexity Computer, we’ve seen single tasks invoke hundreds or even thousands of retrieval operations within a few minutes: a workflow that is impossible for humans but absolutely natural for agents.
In this world, search itself must become agentic, with its building blocks accessible directly as SDKs within the agent harness. We are introducing Search as Code (SaC) as Perplexity’s new reference search architecture.
Introduction
Perplexity's search stack serves thousands of queries each second across our applications and API Platform. In September 2025, we published the first architectural overview of our search systems. Constant innovation within these systems has supported the launch of new offerings such as Search API , Agent API , and Computer , with self-improvement loops optimizing the search stack to better serve users with each passing day.
Traditionally, AI systems have treated search as a monolith: an AI model issues a query, the search engine runs its predefined pipeline, and the model consumes the results as context. For the most part, this worked fine to address the needs of early AI users. Given the relative simplicity of their requests, there was no reason to worry about exactly how the search pipeline was designed, or whether the pipeline's architecture was optimal for the task at hand. The defaults were presumed to be good enough, as were the default interfaces (function calling and MCPs).
Figure 1 | Traditional search architectures expose a single fixed system that models call serially, while Search as Code exposes atomized search primitives that agents compose through generated code.
Yet today this approach grows more outdated with each passing month. Users demand much more than single-shot analysis from AI. They expect agents to complete tasks end-to-end over hours or even days. These tasks can be complex, open-ended, and highly variable in their information needs, and monolithic architectures are buckling under the weight of these demands.
... continue reading