Wow! eBook
Building AI Agents for Finance: Build and deploy robust financial agentic systems with advanced reasoning, architectures, and Python
Author(s): Hanane Dupouy (Author), Fayssal El Mofatiche (Author)
- Publisher: Packt Publishing
- Publication Date: August 27, 2026
- Language: English
- Print length: 756 pages
- ISBN-10: 1837022291
- ISBN-13: 9781837022298
Book Description
Go beyond simple LLM demos and build production-ready finance AI agents with Python, Claude, agentic RAG, multi-agent architectures, evaluation, guardrails, observability, and operations balancing efficiency, reliability, and cost
Free with your book: DRM-free PDF version + access to Packt’s next-gen Reader*
Key Features
- Build finance-focused agents with Python, Claude, OpenAI models, RAG, and tool use
- Apply advanced reasoning and multi-agent architectures to financial workflows
- Implement, evaluate, observe, and govern agents, with a focus on reliability and robustness
Book Description
AI agents are rapidly changing how financial systems analyze information, make decisions, and automate complex workflows. While many resources explain agentic AI concepts, few show how to design and deploy AI agents that work reliably in finance. This book fills that gap.
You will start by learning what AI agents are, how they differ from non-agentic systems, and when agentic architectures are appropriate. You’ll use Python with Claude and OpenAI models across hands-on labs covering design patterns, memory, agentic RAG, framework selection, reasoning paradigms such as ReAct, reflection, self-consistency, and Language Agent Tree Search, and multi-agent collaboration. You’ll then take a deep dive into financial use cases, including fundamental analysis, deep research, trading, insurance, and compliance, using technologies such as LangGraph, Claude Skills, the OpenAI Agents SDK, and LlamaIndex.
You will learn to evaluate agent behavior, calibrate LLM judges, detect drift, and produce model risk reports. Finally, you will focus on operationalizing AI agents responsibly, covering the agent harness and execution loop, observability and tracing, deployment and versioning, guardrails, and governance with human oversight.
By the end, you’ll be able to design, evaluate, and deploy financial AI agents that deliver real business value.
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What you will learn
- Master core AI agent design patterns and apply them to finance
- Compare major AI agentic frameworks and learn how to select the right one
- Explore reasoning paradigms used in agentic workflows
- Design multi-agent orchestration using various architectural styles
- Build financial use cases with hands-on Python labs
- Evaluate and test AI agent behavior effectively
- Implement guardrails, tracing, and observability
- Apply AI agents across fundamental analysis, trading, research, and compliance
Who this book is for
This book is for finance professionals who want to understand and apply AI agents, and for developers and ML practitioners who want to build agentic systems for financial use cases. Financial analysts, quantitative researchers, and technology teams at financial institutions will find working blueprints they can adapt to their own workflows. A working knowledge of Python is required to get the most out of the hands-on labs. No prior experience with AI agents is assumed: the foundations are built from the first chapter, and financial concepts are explained as they are introduced.
Table of Contents
- What Are AI Agents?
- Exploring Design Patterns for AI Agents
- AI Agent Frameworks in Finance
- Building a Fundamental Analysis Agentic System
- Deep Search — Analyst-Grade Research with AI Agents
- Implementing Reasoning Paradigms for Financial Agents
- Multi-Agent Systems and Architectural Styles
- Designing Multi-Agent Trading Systems: From Investment Committee to Adversarial Debate
(N.B. Please use the Read Sample option to see further chapters)
Editorial Reviews
About the Author
Hanane Dupouy is an algorithmic trader specializing in the design and implementation of equity trading strategies focused on precision, efficiency, and scalable execution systems. She holds an engineering degree along with a specialized master’s degree in finance from leading French business schools, and began her career managing teams of data scientists and business intelligence professionals within the banking sector. Over the last years, Hanane has focused extensively on AI-driven financial systems, leveraging machine learning, generative AI, AI agents, and retrieval-augmented generation (RAG) to build and optimize adaptive financial workflows. Hanane shares her expertise through her blog, Machine Learning Basics, where she publishes in-depth tutorials on technical indicators, AI-driven finance, and algorithmic trading best practices. She is also a frequent speaker at industry events, including CFA UK Technology & Innovation Skills webinars, where she discusses practical applications of agentic systems in finance. A CFA Level I & II charterholder, Hanane combines technical rigor with strategic vision to bridge the gap between cutting-edge AI technologies and practical business solutions.
Fayssal El Mofatiche is the Founder and CEO of Flowistic and Finteda, where he focuses on building intelligent, agent-driven systems for financial decision-making and automation. With a strong foundation in finance, technology, and entrepreneurship, he has worked extensively on designing scalable AI architectures that operate reliably in real-world financial contexts.. Fayssal combines hands-on engineering experience with a deep understanding of financial markets, risk, and product strategy. As a CAIA charterholder, he brings a disciplined investment and risk-aware mindset to AI system design. Through his work as a founder, practitioner, and educator, Fayssal helps bridge the gap between advanced AI research and practical, production-ready applications in finance.
