Agents

Research in intellectual property means investigating large amounts of highly complicated inventions and papers. Historically that meant employing large amounts of research teams to scan and evaluate thousands of documents. Research agents break with that historic pattern by letting you spawn intellectual workers on demand to tackle almost any repetitive task a human researcher could do.

You interact with agents by simply giving them a task or a goal and let them work for minutes or hours to complete it. They can search for patents, browse for products, launch web browsers, screenshot websites, download files, build claim charts and store every research finding in tables, notes and dashboards to present you their results.

Before launching an agent you should make a conscious choice about the model you want to use. We offer every state-of-the-art model from all major AI labs, however they all have different costs, as well as strengths and weaknesses. In general you are billed every input and output token that a research agent uses. You can find a detailed overview of the associated costs on the pricing page.

Research agents work in a well-defined data model that is provided as harness to them. The data model is described in detail on this page, but we will give a short overview here as well. The goal of each research agent is to populate a research. A research contains a collection of data records, such as patents, products and other objects, as well as attributes that enrich these records e.g. a custom classification of patents. Records can be mapped to each other to create connections between them. For example to determine the protective scope of patents we create mappings between a product and a patent by building an infringement claim chart for them.

On top of the data the research also contains conclusions and presentations of this data. Each research contains a set of pages that can be one of the following:

• A table of patents, products or another object presents the data records as rows with attributes and mappings as columns. Tables are often the most intuitive way to present research results.
• A dashboard to visualize data with all sorts of charts e.g. line, bar, donut, or complex sankey charts. Mappings between data records can also be charted.
• A note to present research findings as rich-text documents with integrated tables and charts. Often used to define executive summaries or follow up tasks.

Agents inform you about their progress as they run. You can leave the window open or even open and run multiple agents in different windows. While an agent is running, it keeps track of its progress via a to-do list that ticks off as it goes. If you notice the agent going in the wrong direction, you can press control + c at any time to abort the agent and follow up to correct it.

Skills#

Research agents often share common workflows that require the same steps to be completed to reach a result. To unify their approach, we provide a set of standard skills that an agent can learn to complete a specific workflow more effectively.

Skills are fully compatible with your existing skills from Claude. They are basically just a collection of markdown files that describe a particular workflow or extend the agents knowledge. We provide a set of standard skills that cover the most important patent research use cases. Here are some examples of our standard skills:

• Freedom-to-Operate takes one product, design, or idea and works out which third-party patents it might read on.
• Infringement Detection starts from a single patent or a portfolio you hold and finds the products infringing them.
• Portfolio Classification maps a collection of patents into a custom taxonomy of labels or classifications.
• Infringement Claim Charting explains the agent how to draft litigation-grade infringement claim charts for mapping a patent to a product.

There are many more standard skills that we provide to your research agents out of the box. Go to Settings > Skills to explore all of them.

Connectors#

Agents thrive on data. That is why we provide the most important data sources like a global patent database or product search as part of the harness. However, you can extend the data sources of your research agents by adding connectors.

Connectors allow your research agents to read and interact with external services. They are based on the MCP protocol, which means you can plug in any service that provides a MCP server.

We provide a pre-configured set of connectors that you can simply turn on to allow your research agent to access the data source:

• Semantic Scholar: Search 200 million scientific papers with citations and abstracts.
• BioContextAI: Life science databases: Europe PMC, UniProt, ClinicalTrials.gov, openFDA and more.
• EMBL-EBI InterPro: Protein family and domain annotation, with InterProScan for new sequences.
• EMBL-EBI OLS: Biomedical ontology lookup: ChEBI chemicals, GO terms, and more.
• Wolfram: Wolfram Alpha computation and curated knowledge.
• DrugBank: Drug, target, and interaction data from DrugBank.

Memories#

Research agents learn over time to adjust to your research style and preferences. However, they don't train the underlying base model, rather they store a collective set of memory files that other research agents can read and write to share knowledge about their findings.

A memory is simply a short markdown note with a name, e.g. a naming convention you prefer, what a client decided and why, or which assignees turned out to matter in a field. Every agent starts with the list of memory names, reads the ones that bear on its task and writes new memories or edits existing ones as it goes. Research findings do not belong in a memory. They belong in a note inside the research.

Memories are yours and belong to the organization you are working in. Every agent you run there shares them, subagents included, while your colleagues keep their own set. You can keep up to 100 memories per organization. With local storage mode on, they are stored in your browser or on your device instead of in the cloud.

You can read, edit and delete any memory at any time by going to Settings > Memories. New memories are written by the agents themselves.

Subagents#

A research agent can fan-out subagents to delegate work. You can explicitly instruct the agent to spawn subagents to research specific tasks in more detail or simply let the agent decide when to spawn subagents.

Each subagent gets a fresh context window and works in the same research with the same tools, skills, connectors and memories as its parent, on the same model. A single fan-out can start up to 25 subagents, and a subagent cannot spawn subagents of its own. Subagents cannot ask you questions or send e-mails either. They report their results back to the parent agent, which decides what to do next. Like the parent, each subagent is billed per input and output token of the model it runs on.

Active subagents are shown on the bottom of the agent window. While a subagent runs, its row shows the tokens it has used and the time elapsed so far. You can click on each subagent to observe the current state and direction. Upon completion the subagents will disappear from the agent window and report their results to the parent agent.

Local vs Cloud#

Research agents can either run on your local device or in the cloud. You can enable local execution by turning on local storage mode under Settings > Data controls. With local storage mode on, research data, skills, connectors and memories are only stored in your browser or, in the desktop app, on your device. Real-time collaboration and cloud agents are disabled, and research agents run in your browser or in the desktop app instead.

Be aware that the underyling model is cloud-hosted unless you configure an on-device model. We provide every frontier model out of the box and you configure under Settings > Modelswhich model you want to use. If you prefer to use your own API keys, you can also add own Anthropic, OpenAI or Gemini API key, or, in the desktop app, your Claude or ChatGPT subscription. Your keys stay in your browser, or in the operating system's keychain in the desktop app. They are never stored on our servers.

Notifications#

When an agent needs input or wants to share their results, but you don't have the application open, it can send you an e-mail.

There are two kinds of e-mail. A report is sent when an agent finishes a task, one e-mail per task from Patalyze's own address, and an agent can also address it to the members of the research. A question is sent when an agent needs a decision from you and the conversation is not open on your screen: it waits about a minute and then e-mails you the question with a link back to the conversation.

Both can be configured under Settings > Notifications. "Agent reports" can be set to Always, Only critical reports or Off, where critical means that an agent only writes when a run failed or needs a decision to continue. "Agent questions" can be set to Always or Off.

External MCP client#

We already introduced Connectors which allow you to connect your research agents to external MCP servers. You can also switch it around and connect to Patalyze from an external MCP client.

That means that you can use Patalyze from ChatGPT or Claude without the need to use our agent harness or UI. The external MCP client will provide and drive the agent loop while Patalyze provides the data and tools to conduct a professional patent research.

To get started, simply open Claude or ChatGPT and tell them to set up Patalyze. The agent will automatically figure out how to install everything it needs.