Perplexity Patents is changing the patent search process with its autonomous research agents. Users can ask natural-language questions, like “What are the latest patents in AI-powered healthcare?” and receive not just documents but also summaries and follow-up queries. This platform links related terms, helping uncover prior art that typical keyword searches might overlook. The shift from passive retrieval to active reasoning, as noted by IPWatchdog, allows these systems to break down research goals, plan searches, and refine results based on what they find, simulating the way human researchers work. Agentic AI systems differ significantly from traditional generative AI. They incorporate an orchestrator agent that manages tasks and uses feedback to improve outcomes. For a patent application to be successful, it must clearly outline the system’s components and processes. Companies like PatSnap are also entering this space, offering “AI agents” that can automate prior art discovery and patent drafting. With a database of over two billion structured data points, PatSnap’s tools aim to streamline the IP creation process, cutting down on the manual work typically required by research teams. In addition to searching, these agentic systems can now handle drafting tasks. DataGrid reports that AI agents can analyze invention disclosures and draft claims that meet jurisdictional standards, significantly reducing the time needed for initial applications. Despite these advancements, there are risks. While AI tools can enhance efficiency in patent preparation, errors still occur, such as mixed claim types and misaligned descriptions. The American Intellectual Property Law Association emphasizes that while AI can be useful, human oversight remains essential to ensure accuracy.