Perplexity's Deep Research Rewrites Search in Python, Hits 83.8% on BrowseComp

Perplexity just made its most significant research upgrade yet. Deep Research is now a native skill inside Computer, Perplexity's multi-model agent platform, and it is built on a fundamentally new architecture called Search as Code (SaC). The result is a system that does not just search the web for you -- it writes code to orchestrate thousands of search operations in parallel, tailored to each specific question.

The old way was already broken

Traditional search was designed for humans: type a query, get back 10 blue links, and click through. That worked for decades. But in 2026, the primary consumers of search are no longer humans -- they are AI agents. And AI agents do not just need answers. They need to orchestrate complex retrieval workflows with thousands of operations, custom logic, and dynamic strategies tailored to each task.

Perplexity Computer unifies every current AI capability into one system, letting you research, design, code, deploy, and manage any project end-to-end from a single conversation. The problem was that its old Deep Research feature was still treating search as a monolith: fire a query, get a result, repeat. That architecture was becoming a bottleneck.

Search as Code: the model writes the pipeline

Search as Code (SaC) is a system that lets AI agents generate Python to call Perplexity's search stack directly, aiming to cut latency and reduce context pollution versus traditional tool-calling. Instead of the model calling a fixed search endpoint and getting back a blob of results, it now writes code that assembles the entire retrieval pipeline from scratch for each query.

SaC, now the default in the Perplexity Computer product and available via the Agent API, allows models to orchestrate search primitives such as async fan-out, deduplication, filtering, joining, and ranking before results hit model context. Think of it as the difference between ordering a pre-set meal versus cooking from raw ingredients -- the model now has access to every component of the kitchen.

The architecture has three tightly coupled layers:

• Models as the control plane: Models are well-suited to deciding what evidence is needed and how uncertainty should be resolved. Deterministic runtimes are well-suited to batching, parallelism, filtering, ranking, and aggregation.