We are currently witnessing a fundamental shift in the role that AI plays in enterprise operations, transitioning from a system that responds when prompted to one that plans, decides, and acts on its own. This shift has a name: agentic AI. And for business leaders and counsel advising on technology strategy, it deserves serious attention right now.
What Is Agentic AI—and Why Does It Matter?
Generative AI, as most organizations have come to know it, is reactive. You ask a question, it produces an answer. An agentic AI system is different. An AI agent can set its own intermediate objectives, retrieve information, make decisions, take action, and iterate across multiple steps—largely without human prompting at each turn.
Think of the distinction this way: a generative AI tool is a highly capable analyst who waits for your questions. An agentic AI system is more like an employee to whom you can delegate a project, one who will interact with other systems and return with a completed work product—or in some cases simply take the action you would otherwise have taken yourself.
The enterprise use cases are expanding quickly: reconciling invoices in financial services, managing end-to-end customer inquiries in operations, extracting contract data and routing approvals in legal and procurement.
When an AI agent takes an action—approves a refund, triggers a payment, modifies a record—the consequences are operational and potentially financial, not merely informational. A hallucination in a generated memo is an inconvenience, but something that can be caught by a watchful employee. An error made by an autonomous agent operating inside a live business process is something else entirely.
Key Issues for Organizations to Address
Defining the boundaries of autonomy. The most important question for any agentic AI deployment is: at what point must the system pause and involve a human? This threshold should be calibrated to the nature and consequence of the actions the agent is authorized to take. Low-stakes rule-bound transactions may warrant a high degree of autonomy; decisions with material financial, legal, or reputational consequences likely require more friction. Getting this calibration right is both a governance question and increasingly a contractual one.
Accountability when things go wrong. When an agentic system produces an erroneous outcome—particularly if it has taken action across multiple systems—attributing responsibility and calculating harm is considerably more complex than with traditional software failures.
Regulatory dimensions. AI agents that make or influence consequential decisions are drawing heightened attention from regulators. The EU AI Act imposes specific obligations around transparency, human oversight, and risk management for high-risk AI systems. Domestic attention is also growing at the state level. Organizations deploying agentic AI, particularly in regulated industries, should engage counsel early to assess the applicable landscape.
How We Can Help
Morgan Lewis’s technology transactions, outsourcing, and commercial contracts lawyers advise clients across industries on AI strategy, governance, and deal structuring, including the emerging legal and commercial dimensions of agentic AI deployment. If you have questions about the topics discussed above or would like to learn more, please reach out to any member of our team.