The legal profession has long been defined by human judgment — the ability to read a room, weigh competing equities, and exercise discretion that no statute can fully codify. That definition is under pressure. Agentic Artificial Intelligence, a class of systems capable of autonomous reasoning, goal-directed execution, and self-correction, is moving from experimental deployment to operational integration within law firms. The implications are not merely technological. They are deeply legal. Beyond Automation: What Agentic AI Actually Does It is important to distinguish Agentic AI from its predecessors. Traditional Robotic Process Automation (RPA) executes predefined, rule-bound workflows. It is reliable but brittle — any deviation from anticipated inputs causes failure. Agentic AI, built on Large Language Models (LLMs) as reasoning engines, operates differently. It interprets high-level objectives, decomposes them into executable sub-tasks, interfaces with external systems such as legal databases and case management tools, and self-corrects iteratively based on new information. This is not digitisation or even digitalisation. It is delegation — the conferral of decision-making authority upon a non-human system. That distinction carries significant legal weight. Applications in Legal Practice The legal sector is structurally well-suited to Agentic AI adoption. Its operations are precedent-driven, procedurally rigid, and documentation-intensive — precisely the conditions in which AI-assisted reasoning yields efficiency gains. In litigation, autonomous agents can conduct multi-jurisdictional precedent analysis, identify argument gaps, and tailor persuasive briefs to the known preferences of specific courts. In contract lifecycle management, agents can move beyond drafting to autonomous negotiation, real-time risk flagging, and compliance monitoring against evolving regulatory standards. In legal research, multi-step reasoning enables agents to apply precedent to novel fact patterns and generate annotated synthesis reports that would previously require days of associate hours. These capabilities are not futuristic. They are being deployed now. And their deployment is occurring faster than the legal and regulatory frameworks designed to govern them. The Governance Problem The deployment of autonomous agents in legal practice raises three distinct categories of legal concern. The first is professional responsibility. Bar Council rules and analogous professional conduct frameworks across jurisdictions impose duties of competence, supervision, and confidentiality upon lawyers. When an agentic system drafts a pleading, negotiates a clause, or advises on litigation strategy, the supervising advocate remains professionally and ethically responsible for that output. The Bar Council of India Rules, much like the ABA Model Rules in the United States, do not contemplate the outsourcing of legal judgment to autonomous systems — yet that is functionally what is occurring. Firms must urgently develop internal protocols establishing meaningful human-in-the-loop (HITL) oversight at every decision point of material consequence. The second concern is liability allocation. When an autonomous agent makes an erroneous legal determination that causes harm to a client — a miscalculated limitation period, a missed regulatory threshold, a flawed contract risk assessment — the question of who bears liability is not settled law. The agent is not a legal person. The firm that deployed it, the software developer who built it, and the lawyer who relied upon it may each face exposure under different theories: professional negligence, product liability, and contractual breach respectively. India's Information Technology Act, 2000 and the emerging Personal Data Protection framework offer partial guidance but do not comprehensively address AI-induced legal harm. Legislative intervention is overdue. The third concern is data security and privilege. Agentic workflows necessarily involve the processing of privileged client information by third-party AI systems. Legal professional privilege, protected under Section 126 of the Indian Evidence Act, 1872, was designed for human advisors operating within confidential relationships. Whether privilege survives transmission through an AI agent — particularly one hosted on external cloud infrastructure — is an unresolved doctrinal question. Firms deploying agentic tools must conduct rigorous data residency and access-control audits to ensure that privilege is not inadvertently waived. Toward a Regulatory Framework The European Union's AI Act classifies high-risk AI applications in fields including legal interpretation and is a useful reference point. India currently lacks an equivalent sector-specific framework for AI in legal services. The Bar Council of India, the Law Commission, and the Ministry of Electronics and Information Technology should collaborate to develop guidelines that mandate transparency in AI-assisted legal work, establish minimum HITL requirements, and clarify liability rules for autonomous legal agents. Agentic AI will not replace lawyers. But it will fundamentally alter what lawyers do, how they are supervised, and who is accountable when things go wrong. The profession that has spent centuries defining the standards of human judgment must now define the standards by which it delegates that judgment — before the absence of standards defines it instead.