The emergence of artificial intelligence has redefined the boundaries of what software can achieve in knowledge-based professions. From accelerating contract analysis to summarizing discovery documents, the promise of AI in legal, privacy,  and compliance-driven domains is no longer theoretical. It is real.

But most mainstream AI models are not designed for environments where evidence, risk, and regulation intersect. Large language models (LLMs), while powerful in general-purpose applications, present structural challenges when deployed in domains that require explainability, accountability, and security. They were not built with legal defensibility in mind, nor were they trained in environments where audit logs, human oversight, or data sovereignty are mandatory.

Agentic AI offers an alternative suitable for legal use cases, as well as other high-risk disciplines. Unlike traditional AI that responds to prompts, agentic AI operates autonomously. It is capable of pursuing goals, breaking down complex tasks, interacting with systems and humans, and validating its own outputs. It offers the logic and auditability of a seasoned analyst with an efficiency and accuracy unattainable by humans.

Download this whitepaper to learn:

• Why generative AI falls short in regulated environments, like legal and compliance work
• How agentic AI is different from generative AI and LLMs--and how it translates into more defensible outputs
• How organizations can implement AI for legal use cases in a defensible manner