artificial-intelligence-4389372_1280Artificial intelligence (AI) continues to reshape industries, from logistics to health care, but with this transformation comes a steep learning curve for in-house legal teams. Two key concepts — AI Agents and Agentic AI — are central to navigating the legal challenges and opportunities this technology presents. While both terms describe AI applications, their distinctions are critical when crafting governance, compliance, and liability strategies.

Here’s a breakdown of what these terms mean, how they differ, and the key legal issues that in-house lawyers should prioritize.

The Basics: AI Agents Versus Agentic AI

AI Agents are task-focused tools designed to automate repetitive processes or execute predefined instructions. They do not make independent decisions but instead operate within the parameters set by developers. Examples include a chatbot handling basic customer service inquiries and tools like Gmail’s Smart Compose, suggesting responses based on context.

In contrast, Agentic AI is far more autonomous. These systems perceive their environment, reason through complex scenarios, make decisions, and adapt over time. Unlike AI Agents, Agentic AI does not require constant human input to function. Examples include autonomous vehicles navigating traffic in real-time and AI cybersecurity systems detecting and mitigating threats without manual oversight.

Think of AI Agents as rule-followers and Agentic AI as problem-solvers.

Why The Distinction Matters

For in-house lawyers, distinguishing between these AI types is not just semantics — it informs how you assess risks, ensure regulatory compliance, and allocate liability. Here’s why:

  • Operational Scope. AI Agents typically perform predictable, low-risk tasks, while Agentic AI’s autonomy introduces complexities like unexpected outcomes and evolving behavior.
  • Liability. When an AI Agent makes an error, it’s usually easy to trace responsibility to its operator or developer. With Agentic AI, which learns and adapts, pinpointing fault is far more challenging.
  • Compliance. Regulatory frameworks, such as the EU AI Act, often impose stricter requirements on autonomous systems (Agentic AI) due to their higher risk profiles.

Understanding these differences ensures that your legal strategies are tailored to the type of AI in question.

Real-World Applications And Legal Concerns

AI Agents In Practice

  • Customer Support. AI-powered chatbots streamline support but can raise issues like inaccurate responses or biased interactions. Legal teams must ensure compliance with consumer protection laws.
  • Personal Assistants. Tools like Alexa and Siri perform helpful but limited tasks. Data privacy concerns are prevalent, as these systems often handle sensitive user data.

Agentic AI In Practice

  • Health Care. Agentic AI systems analyze complex medical data to assist in diagnoses. Errors could lead to malpractice claims, raising questions about liability and standard of care.
  • Autonomous Vehicles. These systems operate independently, often making life-and-death decisions. Liability for accidents is a major legal gray area, implicating manufacturers, developers, and possibly regulators.

Top Legal Issues to Consider

Liability Frameworks
For AI Agents, liability is usually straightforward — often tied to the deploying company. However, with Agentic AI, where systems operate autonomously and evolve over time, liability can become fragmented. Key considerations include drafting clear indemnification clauses in vendor agreements, requiring ongoing audits of AI system performance, and addressing cross-jurisdictional liability when systems operate internationally.

Regulatory Compliance
Emerging regulations, like the EU AI Act, differentiate between AI’s risk levels. For high-risk applications like Agentic AI in health care or transportation, compliance requirements may include transparent documentation of the AI’s decision-making processes, incorporation of human oversight mechanisms, and regular assessments for bias and safety.

Ethical Considerations
Agentic AI introduces significant ethical questions, such as: how to address biases that AI systems might develop autonomously, and whether AI decisions can be explained in a way that satisfies stakeholders and regulators.

Data Privacy
Both AI types rely heavily on data, raising risks under privacy frameworks like GDPR or CCPA. Ensure that consent is obtained for data collection, that systems have robust cybersecurity measures, and that AI Agents handling sensitive data comply with sector-specific privacy laws (e.g., HIPAA for healthcare).

IP Protection
AI systems can create original outputs, from artwork to software code. Legal teams must evaluate whether these outputs qualify for intellectual property protection and address potential copyright infringement risks.

Actionable Steps For In-House Counsel

To effectively manage AI’s legal and ethical challenges, consider the following:

  • Develop Tailored Contracts. Address unique risks for each AI type, specifying liability, audit rights, and compliance obligations.
  • Implement Governance Policies. Establish internal frameworks for the ethical use of AI, focusing on transparency, accountability, and risk mitigation.
  • Engage Stakeholders. Involve cross-functional teams — including IT, risk management, and compliance — to ensure holistic oversight of AI systems.
  • Monitor Evolving Laws. Stay ahead of AI-specific legislation, particularly in high-risk sectors like transportation, healthcare, and finance.

Looking Ahead

AI Agents and Agentic AI are rapidly advancing, with both offering tremendous potential — and unique legal challenges — for businesses. As the distinction between these systems blurs, legal teams must remain agile, ensuring that their organizations leverage AI responsibly while protecting against liabilities.

For deeper insights into how in-house lawyers can navigate these complex issues while driving innovation, my book, “Product Counsel: Advise, Innovate, and Inspire,” offers practical guidance. From crafting proactive legal strategies to fostering cross-functional collaboration, it equips counsel to address the challenges of AI and other cutting-edge technologies with confidence and creativity.

How is your company adapting to the rise of AI? Have you encountered unexpected legal challenges?

Let’s discuss — share your experiences and insights.


Olga MackOlga V. Mack is a Fellow at CodeX, The Stanford Center for Legal Informatics, and a Generative AI Editor at law.MIT. Olga embraces legal innovation and had dedicated her career to improving and shaping the future of law. She is convinced that the legal profession will emerge even stronger, more resilient, and more inclusive than before by embracing technology. Olga is also an award-winning general counsel, operations professional, startup advisor, public speaker, adjunct professor, and entrepreneur. She authored Get on Board: Earning Your Ticket to a Corporate Board SeatFundamentals of Smart Contract Security, and  Blockchain Value: Transforming Business Models, Society, and Communities. She is working on three books: Visual IQ for Lawyers (ABA 2024), The Rise of Product Lawyers: An Analytical Framework to Systematically Advise Your Clients Throughout the Product Lifecycle (Globe Law and Business 2024), and Legal Operations in the Age of AI and Data (Globe Law and Business 2024). You can follow Olga on LinkedIn and Twitter @olgavmack.

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