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Someone will try to sell you an LLM that manages your money. It will sound confident. Some of it will be made up, and it will not tell you which parts. This primer is about why that happens - an LLM on its own has no data and no calculator - and about the fix, which is not a smarter model. It is the right architecture.
Five ideas, in about twenty-five pages. LLMs are buns, not meat: good at the language, not at the numbers. Most tasks don't need an agent - the complexity ladder tells you when a workflow will do. An agent is four parts - model, tools, instructions, memory - holding three kinds of tools, and only one kind should ever act without a human. Four patterns cover the work: tool calling, ReAct, CodeAct, orchestration. And the demo is not the product - the gap to production is guardrails, approval, and audit, not cleverer models.
No code, no notebooks. Written for anyone who will buy, build, or approve an AI tool that touches a portfolio - and who wants to be able to tell, when shown one, whether it is calling real tools or generating a confident paragraph.
This is the primer to the full book, *AI Agents for Investing* (listed in this catalogue), which builds all of it in runnable code: the tools, the four patterns, a working portfolio assistant, and a no-code path for people who would rather not write Python.
Someone will try to sell you an LLM that manages your money. It will sound confident. Some of it will be made up, and it will not tell you which parts. This primer is about why that happens - an LLM on its own has no data and no calculator - and about the fix, which is not a smarter model. It is the right architecture.
Five ideas, in about twenty-five pages. LLMs are buns, not meat: good at the language, not at the numbers. Most tasks don't need an agent - the complexity ladder tells you when a workflow will do. An agent is four parts - model, tools, instructions, memory - holding three kinds of tools, and only one kind should ever act without a human. Four patterns cover the work: tool calling, ReAct, CodeAct, orchestration. And the demo is not the product - the gap to production is guardrails, approval, and audit, not cleverer models.
No code, no notebooks. Written for anyone who will buy, build, or approve an AI tool that touches a portfolio - and who wants to be able to tell, when shown one, whether it is calling real tools or generating a confident paragraph.
This is the primer to the full book, *AI Agents for Investing* (listed in this catalogue), which builds all of it in runnable code: the tools, the four patterns, a working portfolio assistant, and a no-code path for people who would rather not write Python.
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