Artificial Swarm Intelligence (ASI) describes distributed computational systems in which large numbers of artificial intelligence agents coordinate and optimize collectively, producing outcomes beyond the capacity of any individual model. Emerging from advances in generative and agentic AI, ASI represents a structural shift from isolated tools to networked, goal-directed architectures capable of planning, adaptation, and autonomous execution across platforms and jurisdictions. As these systems enter legal practice, their function extends beyond drafting and research to compliance monitoring, contract lifecycle management, litigation analytics, negotiation modeling, and strategic decision support. When legal reasoning increasingly occurs within hybrid human–machine environments, agency is distributed across interacting agents rather than concentrated in a single actor. Under such conditions, questions of responsibility and liability become correspondingly diffuse, raising complex issues of supervision, attribution, cross-border exposure, and professional accountability. Responding to this shift requires a systematic analysis of risk and the development of governance structures appropriate to agentic technologies. Conventional liability doctrines, grounded in individual intent, proximate cause, and human-centered foreseeability, struggle to account for harms produced through decentralized and emergent system behavior. This CLE therefore introduces a Theory of Distributed Liability to address the analytical challenge of identifying responsibility when no single entity functions as the definitive legal actor. Within this framework, legal evaluation moves toward standards of Algorithmic Accountability, where responsibility is assessed through the design, oversight, and safeguards embedded within the system. A broader jurisprudential inquiry follows how concepts such as intent, causation, accountability, and institutional legitimacy evolve when intelligent systems participate directly in legal workflows. The discussion also introduces Quantum Law Practice as a model of legal operations adapted to computational scale. In this model, legal professionals design, supervise, and audit networks of cognitive systems operating in parallel, transforming legal work into a form of probabilistic and data-driven analysis. The accelerating development of generative, agentic, and swarm-based AI therefore presents significant regulatory and ethical challenges. The lecture concludes with a structured examination of the technological landscape, emerging governance frameworks, and professional competencies required for responsible practice in an era of distributed artificial intelligence.