Increasingly, AI agents—in the operational sense, autonomous software designed to execute a range of tasks—can navigate interfaces, execute transactions, interact with customers, and operate across internal and external systems. Agentic AI refers to the use of multiple agents able to reason through complex goals, orchestrate complex plans to achieve those goals across different systems, and adapt workflows autonomously to manage outcomes end to end. These agents work on behalf of a user (or organization) to accomplish these goals. The law, however, defines agents differently.
Agency, and the term "agent" in the legal sense generally, refers to a fiduciary relationship where one party (the agent) is authorized to act on behalf of another (the principal) to create legal relations with third parties. An agent can bind a principal to contracts or liabilities when acting within their authorized scope, creating a legally binding relationship with third parties. Binding authority can be actual (express or implied) or apparent, where the principal's actions lead third parties to reasonably believe the agent has authority. Ratification arises when a principal, with knowledge of the material facts, later affirms or accepts the benefits of an agent's unauthorized act, thereby treating the act as if it had been authorized from the outset.
Traditional agency law contemplated human parties, or organizations, comprising and led by humans, such as banks and corporate institutions. So, when a human agent "goes rogue" and exceeds the scope of authority, long-standing doctrines provide a structured allocation of liability: the principal may still be bound under apparent authority, while the agents themselves can be held directly liable for unauthorized acts, fraud, or breach of fiduciary duty. In that framework, liability is often shared or shifted, and there exists a clear, legally cognizable actor—the agent—who can bear responsibility.
In contrast, when an AI agent "goes rogue," there is no independent legal actor to absorb or share liability, as U.S. courts have not yet squarely addressed how traditional agency doctrines apply in this new context. Consequently, courts may focus less on the allocation of liability between principal and agent and more on how existing doctrines—such as attribution, apparent authority, negligence, and product liability—may be applied to conduct generated by AI systems. That said, courts in at least one jurisdiction have held a company liable for an inaccurate statement made through its automated customer service system (an AI chatbot). See, e.g., Moffatt v. Air Canada, 2024 BCCRT 149.
Attribution, Authority, and Liability for AI Agents
Whether an AI agent's conduct binds the company depends on applicable agency principles, including attribution, authority, and ratification. Granting an AI system access, permissions, and operational scope may inform the analysis of authority, but it does not exclusively define the boundaries of legal exposure. U.S. courts have yet to definitively rule on whether conduct by an AI agent outside its intended or expressly defined parameters may nonetheless be attributed to the enterprise, particularly where apparent authority, reliance, or subsequent ratification exists.
Agency doctrine addresses authority, attribution, and ratification, but it does not clearly specify when an AI system stands in the legal position of the user and when it does not. This ambiguity implicates questions of authorized versus unauthorized access, particularly where automated conduct interacts with third-party systems subject to contractual or technical restrictions.
Recent litigation illustrates this uncertainty. In Amazon.com Services LLC v. Perplexity AI, Inc. (preliminary injunction stage), the court considered allegations that an AI-driven shopping agent could be considered to have circumvented technical barriers or terms of service, giving rise to unauthorized access and contract-based claims. The court treated the platform's express prohibition on automated access as controlling and indicated that access may be deemed "unauthorized" as a matter of law, even where the user intended or directed the AI system's conduct.
Although agency law permits attribution within actual or apparent authority and may bind a principal through ratification, these doctrines do not expand lawful access vis-à-vis third parties. Agentic AI thus exposes a gap between internal authorization and external legal permissibility; systems may act within granted authority yet engage in conduct the principal cannot lawfully authorize or ratify.
In IP matters, this may include actions such as accepting license terms, accessing restricted technical materials, or making representations regarding intellectual property rights. Authority is not abstract in this setting; it is reflected in the system design, access controls, vendor arrangements, and internal governance choices that determine whether and how the system may interact with protected intellectual property. System conduct is treated as the principal's own when it falls within actual or apparent authority, and even unauthorized acts may bind the principal if they are later accepted or left unchallenged through ratification. Together, authority, attribution, and ratification allocate responsibility, but their application destabilizes where delegated systems operate at capabilities that exceed the legal limits on third-party access.
Assent, Access, and IP Risk in Agentic Systems
Traditional contract principles of notice and assent apply without modification. Courts have enforced agreements formed through automated conduct where terms were reasonably communicated and conduct indicated acceptance. In the agentic AI context, the absence of a human click may not eliminate assent. Where a company deploys an agent into a platform or workflow that conditions access or use on agreement to terms, the company may be bound by the legal consequences of that interaction, including restrictions affecting intellectual property rights, access, and permitted use.
This reveals a broader structural issue. Traditional doctrines of notice and assent assume a bilateral interaction between parties capable of understanding and accepting terms. Agentic systems do not operate within that constraint. They can execute user-directed actions across multiple third-party systems, each governed by its own access restrictions and conditions of use. The result is a separation between internal authorization and external permission. An agent may be properly authorized by the user or enterprise and still act without authorization as to the system it accesses.
Why Agentic AI Raises the Stakes for IP
When applied to agentic AI these principles can lead to several intellectual property (IP) and liability risks when AI agents "go rogue." These risks generally fall within four categories: (1) inbound IP and data acquisition risks, (2) loss or impairment of the company's own assets, (3) external-facing liability risks, and (4) systemic control risks.
(1) Inbound IP and data acquisition risks
The way in which AI agents obtain information may create threshold liability independent of downstream use. As illustrated by recent litigation, an agent's interaction with restricted systems may be deemed unauthorized even where undertaken at the direction of a user or enterprise. This creates a distinct category of exposure: liability may arise not only from what the agent does with information, but from how it accessed that information in the first instance. These risks include unauthorized access, trade secret misappropriation of third-party information, and violations of contractual or license-use restrictions governing data sources.
In the patent context, AI systems trained on competitor materials or instructed to prioritize speed over prudent clearance may generate internal records that reflect deliberate disregard of known risks. These records may, for example, include prompts or instructions directing replication of competitor functionality, logs evidencing reliance on competitor patents or restricted technical disclosures despite identified IP concerns, outputs incorporating patented features after risk flags were raised, or audit trails showing that clearance review was bypassed or suppressed. These records may later be used to support allegations of inducement, willful blindness, or enhanced damages. The concern here is not hypothetical; it arises from the combination of automation and documentation, as AI does not just act, it records. When systems use third-party materials beyond the scope of authorization, they correspondingly intensify inbound acquisition risk.
(2) Loss or impairment of the company's own assets
In the trade secret context, the same systems may transmit or expose confidential information through external integrations, vendor platforms, or customer-facing interactions. The legal standard for protection requires "reasonable measures" to maintain secrecy. Uncontrolled AI use calls those measures into question. These risks include trade secret exposure and the erosion of confidentiality, particularly where uncontrolled outputs or system integrations compromise reasonable secrecy safeguards.
In the inventorship context, the normalization of AI-assisted development increases the importance of evidentiary discipline. Patent law continues to require human inventors and human conception. Companies that cannot demonstrate the role of human inventors, or that lack contemporaneous records distinguishing human contribution from machine output, risk invalidation of patents where the company cannot show human contribution. Related risks include inventorship defects, breakdowns in ownership documentation, and gaps in copyright authorship or assignment where AI-generated content is incorporated into protected works. Compounding this, failures to track open-source use and data provenance may impair the company's ability to assert or defend its own IP and personality rights.
(3) External-facing liability risks
In customer-facing contexts, AI agents may generate statements regarding patent status, exclusivity, or technological capabilities. Inaccurate representations may create exposure under false advertising doctrines or false marking statutes. The scale and speed of AI-driven communication amplify this risk in ways that traditional compliance structures were not designed to address. These risks extend to deceptive or misleading claims and to the unauthorized formation or modification of contractual commitments, as well as the generation of fabricated reviews or endorsements and the creation of digital replicas or deepfakes.
(4) Systemic control risks
These failures include privacy violations, security incidents involving sensitive data, and auditability gaps that prevent reconstruction of how an AI system accessed, processed, or generated particular outputs. Such deficiencies not only create independent regulatory exposure, but also impair the company's ability to demonstrate compliance, intent, and the existence of reasonable safeguards across all other risk categories.
Organizational Failure Risk: Avoiding the Next Corporate Disaster
The more significant risk is not any single legal violation. It is the emergence of a system in which violations become normalized. Historical corporate failures were not defined by isolated misconduct. They were defined by environments in which pressure, opacity, and weak oversight allowed improper practices to become routine. At best, those failures reflected lapses in judgment by identifiable decision makers. At worst, they were amplified by those same decision makers, whose actions increased both the scale of harm and the clarity of causation.
Agentic AI alters that dynamic. Decisions can be operationalized instantly and executed at significant scale. Conduct that once required repeated human involvement can now be carried out through automated systems with minimal additional input. In that setting, ethical and legal constraints risk being displaced by a single instruction. The consequence is not only acceleration, but amplification. What would have been one decision can become hundreds or thousands of actions, each traceable to the same originating choice, without the intervening friction that might otherwise prompt reconsideration.
This scale alters the nature of risk, as it involves more than just a single employee acting improperly. Instead, it is the potential for distributed conduct, executed rapidly and consistently across systems, with each action creating a record. These systems do not merely act; they document. Logs, prompts, outputs, and decision pathways preserve a detailed account of conduct that may later be reconstructed in litigation or an investigation.
The result is not only increased exposure, but amplified exposure. Therefore, a company's catastrophic failure in deploying agentic AI may become the defining case study for this domain. That failure will be analyzed, taught, and used to establish the baseline for evaluating AI governance, responsibility, and ethics. It will shape how regulators respond, how courts interpret conduct, and how future organizations are judged.
Companies that fail to impose meaningful legal oversight on agentic systems risk creating the type of record that will be studied for decades. In that environment, the comparison will not be with a single disgruntled employee, but with a system capable of replicating that risk on a scale in the thousands (or more).
Path Forward: Governance Integral to Go-to-Market Strategy
The response to these risks is not simply better internal policy. It is the recognition that agentic AI operates at the intersection of contract law, agency law, and intellectual property, and that managing that intersection requires legal judgment, not just technical configuration.
This is particularly true where IP is concerned. Decisions about how AI systems are trained, what data they access, how they interact with third parties, and how their outputs are used all implicate patent rights, trade secret protection, and potential infringement exposure. Those decisions are rarely visible at the point where risk is created. They emerge from system design, vendor integration, and operational convenience.
Effective governance, therefore, requires translating legal constraints into technical controls embedded within the system itself.
One emerging model applying this approach is "policy-as-code," in which legal, security, and compliance requirements are expressed in executable rules that constrain system behavior at runtime. Under this approach, agents do not merely receive instructions—they inherit enforceable constraints governing data access, permitted actions, and interaction boundaries.
This implementation-oriented approach to governance relies on several interrelated mechanisms:
- Technical guardrails, including sandboxed environments, API restrictions, and network controls
- Identity and access management frameworks assign discrete identities to agents and enforce least-privilege principles, ensuring that agents operate only within narrowly defined scopes
- Continuous monitoring and logging provide visibility into agent behavior, creating audit trails that support both internal oversight and external accountability
- Human-in-the-loop controls introduce checkpoints for high-risk actions, requiring affirmative approval before an agent may execute decisions with legal or financial consequences
- Life-cycle management processes govern the deployment, updating, and retirement of agents, ensuring that systems do not "drift" in some way, persist beyond their intended use, or operate under outdated assumptions
These mechanisms are not independent safeguards; they collectively define and enforce the scope of agent authority. Governance, in this sense, begins with clearly specifying what an agent is permitted to do and embedding that specification into system design. It requires ensuring that all agent actions are traceable through comprehensive audit logs capable of supporting investigations and dispute resolution. It also requires automating compliance functions, including detection of unauthorized data access, transmission of protected information, or generation of harmful or misleading outputs. Absent these features, governance remains aspirational rather than operational, and legal risk remains unmanaged rather than mitigated.
Addressing that risk requires a level of coordination that most organizations are not structured to provide internally. Aligning AI deployment with IP strategy is key, and ensuring that contractual frameworks reflect actual system behavior and anticipate how courts and regulators will interpret AI-driven conduct under existing legal doctrines will position the organization to better manage risk.
Conclusion
While courts in the near term may look to existing doctrines to assign responsibility where AI agents are deployed without adequate control, the ultimate issue senior executives and general counsel must address is not whether an AI agent can bind a company, but whether the company understands how and where it already does.
For organizations operating in IP-intensive environments, that question cannot be answered through ad hoc internal measures or purely technical solutions. The risks arise from the interaction of multiple legal regimes, including agency, contract, patent, trade secret, and other laws, and from the way those regimes will be applied to systems that act at scale.
Agentic AI does not merely introduce new risks. It compounds existing ones and preserves them in discoverable form. Avoiding those risks requires more than awareness. It requires dynamic and proactive risk management. This includes cross-functional teams and sophisticated, experienced legal counsel working together to identify and control situations where AI agents may "go rogue." Organizations that treat agentic AI as simply a technical tool will address problems after they occur. Those who approach it as a strategic issue will be positioned to avoid becoming the example that others study.