
AI+ Legal Agent™ – Course Outline
Program Overview
The AI+ Legal Agent™ course is designed to equip legal professionals, consultants, and business users with the knowledge and skills to leverage Artificial Intelligence (AI) in legal workflows. The course explores how AI is transforming legal research, contract analysis, compliance, documentation, and decision support while emphasizing ethical and regulatory considerations.
The program focuses on practical application of AI-powered legal assistants and agents to improve efficiency, accuracy, and decision-making in modern legal environments.
Course Objectives
• Understand the fundamentals of AI in the legal domain
• Apply AI tools for legal research and case analysis
• Automate contract review and document drafting processes
• Improve compliance and risk management using AI
• Understand ethical, privacy, and regulatory implications of AI in law
• Work effectively with AI-powered legal assistants and agents
Target Audience
• Lawyers and Legal Consultants
• Legal Assistants and Paralegals
• Compliance Officers
• Corporate Governance Professionals
• Business Owners dealing with contracts and regulations
• AI enthusiasts entering the legal-tech field
Course Duration
• 16–24 Hours (Flexible delivery: 2–3 days or modular sessions)
Assessment & Certification
• Knowledge quizzes
• Practical assignments (contract review / legal research tasks)
• Final evaluation
• AI+ Legal Agent™ Certificate of Completion
Training Methodology
• Instructor-led training (in-person / virtual)
• Case-based learning
Course Modules
Module 1: Introduction to LegalTech and AI Agents
• AI Basics
• What is LegalTech?
• A Brief History of AI
• Why AI in Law?
• Emerging Trends in Legal AI Agents and Intelligent Automation
• Case Study: AI in Legal Drafting (Harvey AI in law firms)
• Case Study: AI-powered Contract Review in Legal Departments
Module 2: What is an AI Agent?
• AI Agents in the Legal Field
• Characteristics of AI Agents
• Difference between AI Agents and AI Tools
• Types of AI Agents (functional overview)
• Types of AI Agents (architecture-based)
• AI agent implementation in legal operations
• Tools and frameworks for Legal AI Agents
• Industry adoption of Legal AI Agents
Module 3: GPT and NLP Foundation for Legal Agents
• NLP fundamentals in legal AI
• Language models and GPT architecture
• Customizing GPT for legal workflows
• Prompt engineering in legal AI systems
Module 4: AI Agents for eDiscovery
• Understanding eDiscovery and automation needs
• AI-powered investigation systems
• Workflow automation in eDiscovery
• AI-assisted legal investigations
Module 5: Contract Review in Legal Workflows
• Contract review fundamentals
• AI contract review agents
• Clause detection and risk identification
• AI-based contract optimization techniques
Module 6: Legal Research Agents
• Definition of legal research agents
• AI-powered legal research workflows
• Case law and statutory research automation
• Real-world legal research applications
Module 7: Compliance & Risk Monitoring Agents
• Compliance and risk monitoring systems
• AI-based regulatory tracking
• Automated risk detection and reporting
• Hands-on compliance scenario analysis
Module 8: Legal Chatbots & Virtual Legal Assistants
• Introduction to legal chatbots
• Use cases in legal practice
• Design principles of legal assistants
• Deployment in law firms and enterprises
Module 9: AI Agents for IP Filing and Patent Drafting
• AI in intellectual property workflows
• Patent drafting automation
• IP management systems using AI
• Workflow automation in IP filing
Module 10: Case Outcome Prediction Agents
• AI in litigation outcome prediction
• Feature engineering for legal prediction
• Predictive analytics in legal strategy
• Multi-agent legal decision systems
Module 11: Ethics, Fairness, and Transparency in Legal AI
• Bias in legal AI systems
• Accountability in AI-driven legal decisions
• Explainability and transparency
• Legal and regulatory frameworks
Module 12: Capstone Project – Building Your AI Legal Agent
• Designing real-world legal AI solutions
• Building an AI legal agent workflow
• Structuring prompts, inputs, and outputs
• Final project presentation and evaluation

