Unlike chatbots and content generators, agentic AI systems can plan, decide, and act autonomously, raising both opportunities and legal exposure. This article explains what agentic AI is, risks it presents, and practical takeaways for business leaders.

What Is Agentic AI?

If you have used ChatGPT, you are familiar with generative AI: you provide a prompt, and the system generates text, images, or code in response. That interaction is reactive. The AI waits for your input, produces content based on its training, and then stops. It cannot take further steps without you.

Agentic AI systems are proactive rather than reactive. If you deploy an agentic AI system to minimize your supply chain costs, that system may proactively identify costly suppliers, provide written notice of contract termination to such suppliers, contact prospective replacement suppliers, and enter into agreements with such new suppliers, all with minimal or no ongoing human direction. In short, generative AI creates content; agentic AI accomplishes tasks.

Critical Business and Legal Risks

That autonomy is exactly what makes these systems valuable and risky. Organizations deploying agentic AI should consider the following risks.

• Loss of Control / Unintended Consequences. The very benefit of agentic AI is also its biggest risk. These systems act without human involvement, and they can take undesired actions with incredible speed. In our supply chain example above, the agent may single-mindedly minimize costs while ignoring whether a replacement supplier is financially stable, geographically practical, or capable of delivering quality goods. It doesn’t require a big imagination to see how agentic AI could go wrong, and go wrong fast.
• Data Privacy and Security. Agentic AI often requires broad access to data and systems to operate effectively. If permissions are not carefully scoped, an agent may access, process, or transmit data well beyond what is necessary for its intended task. This creates risks of unauthorized data exposure, particularly where the agent handles personal data, trade secrets, or privileged information. The principle of least privilege is difficult to enforce when the agent's tasks are dynamic and unpredictable. In privacy-sensitive contexts, these systems may also collect, process, or share personal data without meaningful human review, raising questions about whether adequate notice and consent have been obtained. That risk is especially acute when agents interact directly with individuals, such as customers or employees, and make decisions that affect their rights or interests.
• Liability for Agentic AI Actions. The legal frameworks for agentic AI are still developing, but one principle is becoming clear: no one will escape liability because an AI system was involved. Utah has already enacted legislation prohibiting AI involvement as a defense. Companies deploying these systems in their operations, products, or services should assume they will be held liable for the actions of their AI agents. Courts and regulators are evaluating multiple theories of liability, including agency principles, tort (negligence), product liability, and third-party platform and computer fraud liability.
• Regulatory Compliance. The regulatory landscape is fragmented and evolving. There is no comprehensive US federal AI legislation, but sector specific federal statutes already apply. Antidiscrimination laws, consumer protection statutes, and financial services regulations all create potential exposure for AI-driven decisions. US states have been very active in proposing and enacting legislation addressing varying aspects of AI. Colorado's AI Act takes effect soon, adding compliance requirements for certain high-risk applications. Internationally, the EU AI Act establishes a comprehensive, risk-based regulatory framework with prohibited practices, high-risk categories, and transparency requirements. Organizations operating globally should use the EU's prohibited list of AI use cases as a baseline.

• Reputational and Brand Risk. Use-case-specific considerations are important here. Any organization deploying an AI agent should ask, “Does this use case involve meaningful risk to my organization’s reputation and brand if things were to go wrong?” Deploying an AI agent in a consumer-facing workstream that makes consequential decisions carries much higher brand and reputational risk than a back-office application that never touches a consumer interaction.

• Business Continuity. Integrating agentic AI into a critical workstream creates a dependency that, if disrupted, can impair the business's ability to operate. If the business has not maintained adequate manual processes, institutional knowledge, and fallback procedures, the loss of the AI system can bring the critical workstream to a halt. Over time, as personnel defer to the AI agent and institutional knowledge atrophies, this dependency deepens, making the business increasingly vulnerable to disruption from any interruption in the AI system's availability or performance.

Practical Takeaways for Business Leaders

• Understand what you are deploying. Agentic AI is not a chatbot. It acts, decides, and transacts with limited or no human oversight. Ensure your leadership team understands this distinction and can identify where agentic AI is already in use across the organization. Conduct a rapid audit of existing AI use across your organization to identify gaps before regulators or counterparties do.
• Establish governance before deployment. Before deploying agentic AI, designate a single executive owner (e.g., CIO or General Counsel) and require documented approval for any system that can take external actions, such as transacting, accessing accounts, or sending communications. Consider implementing circuit breakers, real-time monitoring, and escalation protocols. Build in periodic reassessment triggers so that material changes in system capabilities, data access, or third-party integrations prompt renewed review. Finally, reframe governance as a strategic advantage that removes hesitation within the enterprise and enables AI deployment.
• Define human-in-the-loop requirements. Determine which decisions require human approval, and build those controls into system design.
• Prepare for liability. Review your contracts with AI vendors for indemnification, liability caps, and audit rights. Examine whether your existing insurance covers AI-related claims.
• Audit third-party platform interactions. If your agentic AI systems access external websites or platforms, ensure they comply with each platform's terms of service. This includes requirements that AI agents identify themselves and restrictions on automated access. Non-compliance may create exposure under federal and state computer fraud laws, which carry both civil and criminal penalties.
• Invest in AI literacy. The organizations best positioned to manage agentic AI are those where leaders, lawyers, and line employees understand how the technology works and where it can go wrong.
• Build an AI incident response plan. Just as your organization maintains an incident response plan for cybersecurity breaches, you should develop one for AI failures or unexpected autonomous actions. Define how AI incidents are identified, reported, and escalated. Establish what triggers a formal reassessment of the system, and ensure lessons learned are incorporated into updated controls.
• Agentic AI Risk Checklist: Use the following checklist to evaluate your organization's current exposure:
  • Do we have a complete understanding of all agentic AI used within our organization?
  • Do we have a complete understanding of all the data accessed, used or processed by agentic AI systems used within our organization?
  • Do we have human approval thresholds for high-risk AI actions such as transactions, communications, and data access?
  • Do we owe external parties (customers, etc.) who interact with the agentic AI system disclosures and transparency regarding the use of the agentic AI system?
  • Do our AI systems identify themselves as non-human when interacting with external platforms?
  • Are AI actions logged and auditable?
  • Do our vendor contracts include AI-specific indemnities, liability caps, and audit rights?
  • Have we reviewed the terms of service of all third-party platforms our AI systems access?
  • Do we have an incident response plan for AI failures or unexpected autonomous actions?
  • Do we have a process to reassess AI systems when their capabilities, tools, or data access materially change?

If you have questions about agentic AI, AI contracting or AI governance, contact a member of our Artificial Intelligence team to discuss how we can help.

_{This content is made available for educational purposes only and to give you general information and a general understanding of the law, not to provide specific legal advice. By using this content, you understand there is no attorney-client relationship between you and the publisher. The content should not be used as a substitute for competent legal advice from a licensed professional attorney in your state.}