Artificial intelligence (AI) agents are no longer speculative. These autonomous systems are now powering tools and workflows in law, healthcare, finance, manufacturing, cybersecurity, and beyond. As these agents become more intelligent, more autonomous, and more commercially valuable, companies and inventors are asking a critical question: Can we patent AI agents?
The answer is yes—but not without strategic framing, precision in application drafting, and a firm grasp of the law’s current posture. As the technology evolves, so too does the legal framework that determines whether and how intellectual property protections can be secured for agent-based systems.
The Statutory and Doctrinal Framework for AI Patentability
Under 35 U.S.C. § 101, the Patent Act allows patent protection for “any new and useful process, machine, manufacture, or composition of matter.” At first glance, an AI agent—particularly one embodied in software—may appear to fit within this definition. But in practice, the U.S. Supreme Court has narrowed the scope of patentable subject matter by excluding laws of nature, natural phenomena, and abstract ideas.
A core obstacle for many AI-related inventions is the so-called “abstract idea” exclusion, particularly when the AI is implemented in software. To navigate this barrier, courts and the United States Patent and Trademark Office (USPTO) apply the two-step framework set out in Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014). First, the court determines whether the claims are directed to an abstract idea. If they are, the inquiry shifts to whether the claims contain an “inventive concept”—an element or combination of elements sufficient to transform the claim into patent-eligible subject matter. This framework, drawn in part from Mayo Collaborative Services v. Prometheus Labs., Inc., 566 U.S. 66 (2012), is now the touchstone of eligibility analysis in AI cases.
This legal test does not prohibit software or AI patents per se. The critical issue is whether the claimed invention applies AI in a specific, technical manner that improves the functioning of a computer or another technological process. This was clarified in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), where claims on a self-referential table used in a database were upheld as patentable because they improved the computer’s own functionality.
That theme was reinforced in McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), where an AI-driven animation tool used rules to automate lip-synching in animated characters. The court found the invention patent-eligible because it applied specific, unconventional rules in a novel way to produce a technical improvement in animation. McRO cautioned against dismissing software claims as abstract merely because they involve automation. It emphasized the importance of describing how the invention achieves a technical solution.
Additionally, Diamond v. Diehr, 450 U.S. 175 (1981), remains foundational. There, the Supreme Court held that a process employing a mathematical formula to control the curing of rubber was patentable because it was directed to a physical, transformative process. The Court explained, “a novel and useful structure created with the aid of knowledge of scientific truth may be patentable,” which is especially relevant for AI agents integrated into physical systems, robotics, or diagnostic machines.
Other key decisions like BASCOM Global Internet Services v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016), further refined Alice’s second step. BASCOM upheld a filtering system deployed in an unconventional manner across a distributed network, ruling that the claim’s “non-generic arrangement” was sufficient to survive scrutiny. This case is directly applicable to AI agent architecturesthat rely on distributed reasoning, federated learning, or agent swarms deployed across cloud nodes.[i]
Applying the Law: Real-World Scenarios of AI Agent Innovations
Consider a legal AI agent that performs natural language processing on pleadings, extracts claims, compares them to court records, and drafts proposed responses or motions. On the surface, such a system may seem like a mere automation of human reasoning—a red flag for abstraction. However, if the inventor can show that the system improves the efficiency, accuracy, or computational speed of case preparation, and if that improvement is rooted in a novel software architecture or learning method, the system becomes much more likely to clear “the § 101 hurdle” (i.e, an inventive concept sufficient to confer patent eligibility under 35 U.S.C. § 101).
A second example might involve an AI-based diagnostic system that analyzes speech data to identify early signs of neurodegenerative disease. If the invention includes a new ensemble learning structure, signal filtering technique, or interpretable prediction interface, and if these elements are described with technical specificity, patent eligibility becomes far more likely under the Enfish-McRO-BASCOMframework.
By contrast, In re TLI Communications LLC Patent Litigation, 823 F.3d 607 (Fed. Cir. 2016), offers a cautionary tale. There, claims describing attaching classification data to digital images and uploading them to a server were deemed patent-ineligible. The Federal Circuit ruled that merely implementing a conventional function—data storage—on a generic computer does not render the concept patentable. TLI serves as a clear example of what happens when an AI-related claim lacks both technical improvement and inventive architecture.
USPTO Guidance and Enablement Considerations
The USPTO has tried to harmonize its review process through guidance documents issued in 2019, 2021, and most recently in 2024. These guidelines clarify that inventions implementing abstract ideas, such as algorithms or data analysis methods, may still be patentable if they demonstrate integration into a practical application. This includes solving a recognized technical problem using a specific technological solution.
The Office has warned, however, that AI-related applications must satisfy the requirements of 35 U.S.C. § 112, which mandatessufficient written description and enablement. Vague references to “machine learning” or “training data” without describing how the model operates, learns, or generalizes may doom a claim—even if the concept is patent-eligible in principle. This has become particularly problematic in applications involving Generative AI,[ii]where nondeterminism is inherent and reproducibility may be difficult without full disclosure of training data, model parameters, or inferencing logic. Much of this information is not readily available to probably the vast majority of Agentic AI[iii] creators, who use platforms such as ChatGPT for the very reason that this depth of technical knowledge is not needed to create agents.
The Human Inventor Requirement
In Thaler v. Vidal, 43 F.4th 1207 (Fed. Cir. 2022), the Federal Circuit addressed whether an artificial intelligence system could be credited as an “inventor” on a U.S. patent application. The court answered in the negative, holding that inventorship under U.S. law requires a natural person. This decision aligns with international trends and has major implications for patenting outputs of generative agents or autonomous systems. While AI can assist in the invention process, the conception of the invention must still be attributed to a human being under current law. In actual practice, based on the evolving capabilities of Generative and Agentic AI systems, this distinction is at the crux of the issue and is likely to be the focus of future decisions and analysis as this area of patent law will further develop as the use of these modalities continues to proliferate, and is indeed proliferating at a rapidly increasing pace into the general population.
Strategic Recommendations for Protecting AI Agent Innovations
To maximize the chance of securing patent rights in AI agents, inventors must focus on specificity, technical improvement, and grounded claim language. It is essential to describe the system’s architecture in detail—including training methods, data processing pipelines, inference engines, and human-agent interfaces. Ironically, effective use of the AI Agent may well be the best source of generating the descriptions and data and necessary to make these claims successfully.
Where possible, claim drafting should highlight improvements to system performance, reliability, scalability, or accuracy. AI Agents deployed at the edge (e.g., on IoT devices[iv]), agents that adapt in real-time to human feedback, or agents that reduce computing overhead through model optimization may present strong technical arguments under Enfish or BASCOM.
Inventors should also consider patenting system components independently. For example, an agent’s decision-making logic, knowledge representation strategy, or adaptive learning module may each constitute a protectable invention. Even if the broader system is complex, modularizing innovation for protection can hedge against § 101 and § 112 issues.
At the same time, trade secrets may offer complementary protection for aspects such as model weights, tuning parameters, or training datasets. These are often difficult to reverse-engineer and may be better preserved through confidentiality and access controls, particularly where source code is not disclosed in patent applications.
Leading with the Law, Not Behind It
AI agents represent a technological frontier—and intellectual property law is catching up. The good news is that patent law, especially as clarified in the Federal Circuit’s decisions, supports the patentability of AI inventions that demonstrate specific, technological contributions. But the standards are rigorous. Inventors must show more than automation; they must show technical innovation. By integrating sound legal theory with practical innovation strategy—and by using the right case law and USPTO guidelines to frame their inventions—companies can protect the very AI agents that now power their business models. The race is not just to build better AI, but to secure it. For those prepared to navigate the law, the tools are in pla
[i]The phrase “agent architectures that rely on distributed reasoning, federated learning, or agent swarms deployed across cloud nodes” refers to a class of artificial intelligence (AI) systems designed for scalability, collaboration, and decentralized operation. Below is a breakdown of the key terms and their legal relevance, particularly in the context of patent eligibility under Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014), and clarified in BASCOM Global Internet Services v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016):
1. Agent Architectures: These are software systems composed of multiple agents—autonomous units (often software programs) that can perceive their environment, make decisions, and act toward achieving specific goals. In the AI context, these agents often collaborate to solve complex tasks.
2. Distributed Reasoning: A form of computation where reasoning tasks are shared among multiple agents or nodes across a network. Enables AI systems to process information in parallel, increasing efficiency and fault tolerance. From a patent perspective, a non-conventional deployment of reasoning across multiple nodes—similar to BASCOM‘s filtering across a distributed system—may help establish an inventive concept under Alice step two.
3. Federated Learning: A decentralized machine learning approach where multiple devices or servers collaboratively train a model without sharing their raw data. Smartphones training a shared language model without uploading private messages. Preserves privacy and reduces bandwidth usage. If implemented in a novel and non-generic way, this structure could reflect a technological improvement over traditional centralized AI systems, supporting patent eligibility.
4. Agent Swarms: Inspired by swarm intelligence (e.g., bees or ants), this refers to a group of AI agents working collectively, often without centralized control. Used in robotics, search-and-rescue operations, or cloud-based problem solving. A unique coordination strategy across cloud nodes may be considered a specific implementation that transforms an abstract idea into a patent-eligible invention.
5. Deployed Across Cloud Nodes: Cloud nodes refer to the distributed computing resources (servers, containers, virtual machines) that make up a cloud environment. Hosting AI agents or systems across these nodes allows for scalability and robustness. As in BASCOM, if the system’s deployment across nodes involves a “non-generic arrangement of components,” it may satisfy Alice step two.
Summary of Legal Relevance
In BASCOM, the Federal Circuit emphasized that while the filtering idea was abstract (Step One), the “specific, discrete implementation of the abstract idea” (i.e., deploying it in a novel manner across a distributed architecture) was sufficient to survive Alice Step Two. Similarly, AI systems that feature distributed reasoning, federated learning, or agent swarms implemented in non-generic ways across cloud infrastructure could demonstrate an inventive concept that transforms the abstract idea into a patent-eligible application.
[ii] Generative AI refers to a class of artificial intelligence systems that are designed to produce new content by learning patterns from large datasets. These systems can generate text, images, audio, video, or code that resembles human-created material. The underlying models, often based on deep learning and neural networks, are trained on vast amounts of data and are capable of mimicking the style, structure, and semantics of that data in their outputs. For example, a generative AI system like ChatGPT can draft emails, legal memos, or marketing copy, while image-generation tools like DALL·E can create entirely new visuals based on textual descriptions. The defining characteristic of generative AI is its content-creation capability, which is usually activated in response to a prompt or instruction provided by a user. These systems are highly effective in augmenting human creativity and productivity, but they typically require a user to initiate each task, making them reactive rather than proactive.
[iii]Agentic AI refers to artificial intelligence that exhibits agency—that is, the ability to make autonomous decisions and pursue goals over time, often in dynamic environments. Rather than merely responding to prompts, agentic AI systems can plan, initiate, and adapt their actions with minimal human intervention. They are capable of breaking down complex objectives into subtasks, prioritizing actions, and using available tools or APIs to complete tasks in a logical sequence. An example of agentic AI might be an autonomous legal assistant that monitors a docket, drafts a responsive pleading, files it with the court, and notifies a human attorney of the outcome—all without being prompted to do each of those steps individually. This form of AI operates with a greater degree of independence and is better suited to workflows requiring continuous decision-making or complex task execution.
The key distinction between the Agentic AI and Generative AI lies in their operational nature. Generative AI creates content in response to input, whereas agentic AI exhibits goal-oriented behavior that may involve generating content, interacting with systems, and making decisions along the way. Importantly, modern AI applications are increasingly blending these capabilities. A generative AI model may be embedded within an agentic framework to create powerful hybrid systems that can both generate content and execute tasks autonomously. While generative AI excels at producing human-like content, agentic AI is characterized by its ability to act independently, pursue goals, and engage in multi-step problem-solving. Together, they represent two major trajectories in the evolution of artificial intelligence.
[iv]Internet of Things (IoT) devices are physical objects that are embedded with sensors, software, and communication technologies, allowing them to connect to the internet and interact with other devices or systems. These devices can collect data from their environment—such as temperature, motion, or location—and transmit that data over a network, often in real time. In many cases, they are also capable of receiving instructions or updates remotely and performing actions accordingly, such as turning on lights, adjusting thermostats, or triggering alarms.
Examples of IoT devices include smart home products like thermostats and voice assistants, wearable health monitors such as fitness trackers and glucose meters, industrial equipment outfitted with predictive maintenance sensors, and smart city infrastructure like traffic cameras and connected streetlights. These devices are typically part of a larger distributed system, where multiple nodes (devices) work together by communicating over cloud networks or local wireless systems.
From a legal and patentability perspective, IoT technology often raises important considerations under the framework established byAlice Corp. v. CLS Bank. Courts and the USPTO have scrutinized whether claims involving IoT systems are directed merely to abstract ideas or whether they embody an inventive concept through a novel and non-generic implementation. For instance, in BASCOM Global Internet Services v. AT&T Mobility, the Federal Circuit upheld claims that involved filtering internet content in a distributed network, emphasizing that the specific and unconventional arrangement of components made the invention patent-eligible. Similarly, an IoT architecture that processes or manages data in a unique way across distributed nodes may also meet the eligibility requirements under Alice step two.
Appendix
Engagement Checklist for Clients Developing AI Products
Purpose: Guide early-stage clients through legal and IP issues in AI product development.
☐ 1. Initial AI Product Scoping
- ☐ Identify the core functionality of the AI system (e.g., predictive, generative, decision-making, autonomous agent).
- ☐ Confirm whether the AI performs tasks that were previously human-driven.
- ☐ Document user-facing interfaces, data flow, and intended commercial use.
☐ 2. Data Considerations
- ☐ Is the training data owned, licensed, open-source, or scraped?
- ☐ Are there identifiable individuals in the dataset (PII/biometric risks)?
- ☐ Has data provenance and compliance with privacy regulations (e.g., CCPA, GDPR, HIPAA) been established?
☐ 3. Model and Technical Architecture
- ☐ Identify which components are custom-developed versus third-party (e.g., open-source models like GPT, TensorFlow modules).
- ☐ Map out core innovations: model architecture, feedback loops, learning algorithms, deployment method (cloud, edge, hybrid).
- ☐ Determine whether AI agent behavior adapts over time (relevant for § 112 enablement).
☐ 4. IP Audit and Inventorship
- ☐ List all technical team members involved in conception of novel algorithms or systems.
- ☐ Check whether any AI outputs may qualify as IP or whether human inventorship can be asserted.
- ☐ Confirm employment/IP assignment agreements are in place for all inventors and contractors.
☐ 5. Patent Strategy Alignment
- ☐ Conduct a freedom-to-operate (FTO) search for key functions or models.
- ☐ Assess whether the system improves computer functionality, solves a technical problem, or uses a non-generic architecture.
- ☐ Evaluate trade secret versus patent protection for model parameters, weights, and training methods.
☐ 6. Business Objectives and IP Leverage
- ☐ Align IP protection with go-to-market strategy (licensing, exclusivity, investor due diligence).
- ☐ Consider international filings under PCT for strategic markets.
- ☐ Prepare defensibility materials for funding rounds, M&A, or partnerships.
Patent Roadmap Template for Agent-Based AI Systems
Purpose: Help AI-focused companies plan the stages of innovation capture, protection, and patent prosecution.
Phase 1: Invention Identification & Evaluation
Goal: Define what aspects of your AI agent are novel and potentially patentable.
- Describe the AI agent’s inputs, learning method, and outputs.
- Specify any technical improvement: reduced latency, enhanced model explainability, modularity, etc.
- Is the agent embodied in a system (e.g., IoT device, robotic system), not just software?
Deliverables:
- Technical white paper
- Draft claim elements
- Inventor declarations
Phase 2: Legal Filtering
Goal: Apply eligibility standards under Alice/Mayo and § 112.
- Confirm that claims are not directed to abstract ideas alone.
- Check whether the claims include an inventive concept in their arrangement or architecture (see BASCOM).
- Confirm enablement: would a skilled AI engineer be able to build it from the disclosure?
Deliverables:
- Patentability opinion or pre-filing memo
- Invention disclosure forms
- Inventorship chart
Phase 3: Application Drafting
Goal: Develop a robust U.S. patent application.
- Include flowcharts, system diagrams, training vs. inference distinctions.
- Describe model parameters, adaptation logic, edge-case behavior.
- Consider filing multiple claim types (method, system, computer-readable medium).
Deliverables:
- U.S. provisional or non-provisional application
- Claims drafted to map onto system modules or agent behaviors
- Trade secret retention memo (for non-disclosed elements)
Phase 4: Filing Strategy and Prosecution
Goal: Execute a strategic global and national patent plan.
- File U.S. provisional first if in early stage.
- File PCT within 12 months for foreign priority.
- Prepare to argue Enfish, McRO, Diehr, and Visual Memoryduring prosecution.
Deliverables:
- Filing confirmations
- Examiner interview logs
- Claim amendments per Office Action responses
Phase 5: Post-Filing Portfolio Management
Goal: Use granted or pending claims for leverage.
- Monitor competitor filings (e.g., via USPTO PAIR, Google Patents).
- Update claims as AI agent evolves (CIP or continuation).
- Enforce or license based on scope of granted protection.
Deliverables:
- Patent prosecution history files
- Portfolio map by agent subsystem or feature
- License and enforcement strategy document
Suggested Claim Language Building Blocks for AI Agents
“A method performed by an autonomous agent comprising: receiving sensor input; generating a task response using a neural model trained on context-specific data; and executing said task autonomously via an actuator module…”
“A system comprising: an adaptive AI module configured to interact with human input via a multimodal interface, wherein the system modifies its decision policy based on real-time user feedback…”
