Agentic AI Meets Patent Search: A New Paradigm for Innovation

For decades, inventors, practitioners, and researchers alike have faced the same tradeoff. Free tools surface only the most obvious references, missing decisive prior art. Professional platforms offer depth, but require significant training and demand five-figure subscriptions. The patent system promises to promote innovation by making knowledge accessible. But to fully realize this vision, patent knowledge must be available on tap to everyone who needs it, in whichever form best serves each individual user.

Today’s AI technology has unlocked the possibility of universal access to professional-grade patent intelligence. AI systems are transforming knowledge industries by accelerating complex, human-directed analyses with AI models. However, preexisting AI systems aren’t optimized for the unique needs of patent users and the bespoke nature of patent documents and datasets. Without tight integration between frontier AI intelligence and the patent domain, today’s AI falls short in serving the patent community.

This status quo is now changing thanks to new agentic research techniques pioneered by America’s leading AI companies.

Understanding Agentic AI: Beyond Boolean

Agentic research systems decompose a user’s queries into discrete information retrieval and synthesis workflows. The results of these workflows are then used to guide the AI system’s own further exploration, ultimately leading to the formulation of an answer supported by a traceable research record and authoritative source documents. To see why this matters for the IP community, we first need to examine what makes agentic AI fundamentally different from the tools practitioners have relied on for years.

Prior to agentic AI, patent researchers relied largely on lexical and semantic search systems. Lexical systems rely on keywords and logical operators to surface results from a database, while semantic systems encode entire phrases and paragraphs into mathematical representations called “embeddings”. Both of these systems operate on what we call a “single-turn” approach: send a query, match against documents, and return those documents back to the user.

In contrast, agentic AI hinges on “multi-turn” approaches that better represent the way that smart humans go about retrieving and synthesizing information. Agentic AI systems can frame problems, gather data, design search strategies, and iterate toward better results with multiple queries building towards an answer. It moves searching from traditional standalone queries to goal-directed workflows.

This distinction matters in patent practice. Traditional searches require translating research questions into complex Boolean strings, classification codes, and citation trails. A practitioner investigating wireless charging for EVs might craft intricate combinations of terms and filters, then manually triage hundreds of results.

Early generative AI solutions eased this painstaking process by enabling natural-language queries of a few words but were still limited to individual search attempts. Agentic systems go further: they choose search strategies based on art-specific nuances, explore multiple paths in parallel, and refine based on relevance. The latest systems can even provide reasoned responses with direct citations to source patents.

The practical impact is broad. Attorneys running freedom-to-operate searches no longer need to enumerate every synonym up front. R&D teams can explore adjacent spaces without mastering classification schemes. Founders, entrepreneurs, and investors can ask patent-related questions in plain language. By understanding intent and context, agentic AI surfaces prior art that rigid methods overlook, making patent information genuinely explorable.

From Theory to Practice: Patent Search in Production

Production systems have to handle the realities of the patent domain: complex relationships between documents, relationships between claims and specifications, and terminology that shifts between fields and across eras. Recent advances in AI make this feasible. Frontier AI models trained on patent datasets are able to grasp each of these domains-specific nuances and connect the dots. These models and methods generalize beyond any specific area of art, resulting in an elegant system design that does not require brittle heuristics (for instance, domain-specific synonym maps) that inevitably degrade with the passage of time.

Equally important is transparency. Results should include direct citations and user-friendly explanations, so users can verify why a document was returned and how it maps to the question. Importantly, users should be able to trace each step of an agentic research workflow, seeing each time the agent decides to make a search along with the full results of that search.

Agentic Patent Search Has Arrived

For the first time, comprehensive patent research is available not only for specialists, but for the broader IP and innovation community. The question is no longer whether agentic AI will change patent practice, but how the community will adopt it. Making patent intelligence universally accessible democratizes innovation by making technical knowledge easier to find, understand, and trust.

Click here to learn more about Perplexity Patents, the world’s first agentic research system developed specifically for the patent domain, available in beta starting October 30.