As companies begin to move beyond large language model (LLM)-powered assistants into fully autonomous agents—AI systems that can plan, take actions, and adapt without human-in-the-loop—legal and privacy teams must be aware of the use cases and the risks that come with them.

What is Agentic AI?

Agentic AI refers to AI systems—often built using LLMs but not limited to them—that can take independent, goal-directed actions across digital environments. These systems can plan tasks, make decisions, adapt based on results, and interact with software tools or systems with little or no human intervention.

Agentic AI often blends LLMs with other components like memory, retrieval, application programming interfaces (APIs), and reasoning modules to operate semi-autonomously. It goes beyond chat interfaces and can initiate real actions—inside business applications, internal databases, or even external platforms.

For example:

• An agent that processes inbound email, classifies the request, files a ticket, and schedules a response—all autonomously.
• A healthcare agent that transcribes provider dictations, updates the electronic health record , and drafts follow-up communications.
• A research agent that searches internal knowledge bases, summarizes results, and proposes next steps in a regulatory analysis.

These systems aren’t just helping users write emails or summarize docs. In some cases, they’re initiating workflows, modifying records, making decisions, and interacting directly with enterprise systems, third-party APIs, and internal data environments. Here are a handful of issues that legal and privacy teams should be tracking now.

1. System Terms of Use Are (Still) Built for Humans
   Most third-party platforms—whether cloud apps, SaaS platforms, enterprise tools, or APIs—were not designed for autonomous agents. Terms of service often restrict access to human users or prohibit automated tools altogether. When an agent accesses systems, modifies records, makes queries, or connects data across systems, that may breach current contractual limits.

   Takeaway: Review system terms and licensing agreements for automation restrictions. If you plan to deploy agents, negotiate permission or amend access terms in writing.

    

2. Liability Flows Through You
   If an agent triggers errors—like deleting records, misusing credentials, overloading systems, or pulling data in ways that breach policy—your company is still responsible. There’s rarely contractual coverage or indemnity language that contemplates AI acting on your behalf across platforms.

   Takeaway: Treat agentic systems like high-privilege users. You need clear boundaries around what they’re allowed to do and internal accountability for oversight.

    

3. Privacy Impacts Are Underexplored
   Agentic AI creates new data privacy exposure. These systems may access sensitive data, make inferences, or combine data sources in ways your existing data processing agreements and compliance processes don’t cover. In addition, they often operate without strong logs, making audit or breach response difficult.

   Takeaway: Treat autonomous agents as processors. Run data protection impact assessments . Map data access and flow. Limit scope and tie all actions to traceable logs.

    

4. Regulators Will Expect You to Stay in Control of Agents’ Decision-Making and Data Processing
   If an AI agent makes decisions that affect consumers, processes personal data, or impacts fairness or transparency, it will potentially implicate a variety of laws and regulations, such as the FTC Act and state unfair and deceptive acts and practices laws, privacy laws and regulations – including the GDPR and the CCPA’s forthcoming automated decision-making technology regulations, and AI-specific laws and regulations such as the Colorado AI Act and the EU AI Act. Your company is responsible for ensuring your agents’ compliance with these and other laws and regulations. All this said, we are clearly headed, at least from a U.S. perspective, towards a hands-off regulatory approach and pro-business environment when it comes to AI. This is certainly true from a federal perspective and also perhaps from a state perspective, if the One Big Beautiful Bill Act (H.R. 1) – which purports to ban states from regulating AI for 10 years – is passed by Congress.

   Exemplary enforcement hooks:
   • AI agents making consequential decisions on your behalf without providing proper notice or privacy rights
   • Material deception about whether humans or AI are making decisions
   • Use of sensitive data in ways that violate prior notice or consent
   • Lack of accountability for outcomes from automated systems

   Takeaway: If an agent operates on consumer-facing data or makes consequential decisions, treat it like any other high-risk algorithm. Monitor, test, disclose.

    

5. Audit and Explainability Gaps Are Real
   You may not be able to explain why an agent did what it did—because the system is goal-directed, not rule-bound. Many enterprise systems don’t separate human and agent activity, and internal logs may be incomplete.

   Takeaway: Layer audit and observability more broadly than just the system the agent touches. Require rollbacks, alerts, and human override paths.

    

6. No One Owns This Yet
   Agentic AI is crossing boundaries—legal, privacy, InfoSec, and engineering. But, without a designated policy owner, these tools could be deployed ad hoc without legal input.

   Takeaway: Create a simple policy for agent deployment approvals, access controls, and post-deployment reviews. Assign a directly responsible individual.

The Bottom Line

Agentic AI isn’t theoretical. It’s being shipped into business operations quietly—through pilot projects, dev team prototypes, and platform-native tooling. Legal and privacy teams should engage early, set guardrails, and help the business use these systems responsibly.