An open-source textbook bridging cutting-edge AI technology with practical applications in legal and financial domains. Designed for practitioners, regulators, academics, and technology builders working at the intersection of artificial intelligence, law, and finance.

Project Overview

Artificial Intelligence for Law and Finance: A Modern Textbook at the Intersection of AI, Law, and Finance addresses the growing need for comprehensive educational resources as AI transforms professional practice. The book provides both theoretical foundations and practical guidance for deploying AI systems responsibly in high-stakes domains.

Target Audience

The textbook serves four primary reader groups:

Legal Professionals: Lawyers, compliance officers, and legal operations specialists
Financial Practitioners: Analysts, risk managers, and financial advisors
Academic Researchers: Graduate students and faculty in law, finance, and computer science
Technology Builders: Entrepreneurs and engineers building AI solutions for regulated industries

Part I: Foundations — LLMs and Prompting

LLM Primer and Mechanics — Understanding transformer architecture and model behavior
Conversations and Reasoning — Multi-turn interactions and chain-of-thought techniques
Structured Outputs and Tool Use — Function calling, JSON schemas, and external integrations
Multimodal Fundamentals — Vision, audio, and document understanding
Prompt Design, Evaluation, and Optimization — Systematic approaches to prompt engineering

Part II: Agents and Agentic Systems

What Is an Agent? — Conceptual primer and history of agents and agentic AI
How to Build an Agent — Architecture patterns and implementation strategies
How to Govern an Agent — Oversight frameworks, escalation protocols, and compliance

Part III: Knowledge Graphs & Semantic Web

Foundations for Law and Finance — Ontologies, taxonomies, and semantic modeling
Operations with LLMs — Graph-augmented retrieval and knowledge integration

Published Chapters

Individual chapters are released as working papers as they reach completion:

“What is an Agent? A Conceptual Primer and History of Agents and Agentic AI”

Authors: Michael J. Bommarito II, Jillian Bommarito, Daniel Martin Katz
Published: November 2025
Available on SSRN

This 60-page chapter synthesizes nearly a century of scholarship across eight disciplines—philosophy, law, economics, cognitive science, and computer science—to propose a three-level hierarchy of agency:

Level 1 (Agent): Goal, Perception, and Action (GPA)
Level 2 (Agentic System): Adds Iteration, Adaptation, and Termination (IAT)
Level 3 (Agentic AI): Fulfills all six properties using AI/LLMs for planning and orchestration

Key Themes

Professional Safeguards

The book emphasizes responsible deployment in high-stakes domains:

Attribution: Clear provenance for AI-generated content
Escalation Protocols: When and how to involve human experts
Confidentiality Controls: Protecting privileged information
Audit Trails: Maintaining accountability in automated workflows

Autonomy Spectrum

Analysis of the continuum between:

Delegated Proxies: AI systems acting under explicit human direction
Self-Directed Entities: Systems with greater operational independence

Practical Evaluation

Rubrics to help practitioners distinguish:

Genuine agentic systems from sophisticated tools
Single-shot chatbots from iterative agents
Marketing claims from technical capabilities

Open Source Commitment

The textbook is released under Creative Commons Attribution 4.0 International (CC-BY-4.0), enabling:

Free access for students and practitioners worldwide
Adaptation for classroom and training use
Translation and localization efforts
Community contributions and improvements

Resources

Website: ai4lf.com (launching soon)
GitHub Repository: ai-law-finance-book
Full Draft PDF: Available in the repository
Individual Chapter PDFs: Released as working papers

Impact

This project addresses critical gaps in professional education:

Definitional Clarity: Cutting through hype to establish rigorous frameworks for understanding AI capabilities
Cross-Disciplinary Synthesis: Bridging technical AI research with legal and financial practice
Practical Guidance: Moving beyond theory to actionable deployment strategies
Professional Standards: Establishing norms for responsible AI use in regulated industries

The book demonstrates that effective AI education for professionals requires both technical depth and domain expertise, setting a new standard for interdisciplinary AI resources.