The Wealth Manager's Guide to AI in 2026
Context, Security, Strategy, and the Decisions Ahead (Vol. 2 of AI in Wealth Series)
If you haven’t read Volume 1, start there — it covers how LLMs actually work, the stateless model and stateful harness, RAG, agents, tools, MCP, and what a day with AI in your practice actually looks like. Everything below builds on that foundation.
In Volume 1, we established the machinery: what a Large Language Model actually is (a frozen mathematical function, not a thinking entity), how the harness — the software layer around the model — creates the illusion of memory and continuity, how RAG grounds the model in your firm’s own documents, and how agents use tools, skills, and MCP to move from answering questions to autonomously doing work. We walked through John Doe’s Monday morning to see what changes — and what doesn’t — when agents are in the loop.
Now we turn to the harder questions. The ones that will actually determine whether AI transforms your practice or just adds another line item to your technology budget.
Part III — From Chatbots to Agents (continued)
Context Engineering — The Discipline That Matters Most
We need to talk about the hardest problem in AI. Not the hardest technical problem—the hardest practical problem. The one that will determine which firms get transformative value from AI and which ones spend millions on tools that underperform.
The problem is this: how do you maintain high-signal, trustworthy, actionable context in long-running agent loops under finite attention and changing state?
Let me unpack that in plain language.
An AI agent working on a complex task needs information to do its job—client data, regulatory requirements, market conditions, your firm’s policies, the history of what it’s already done. All of that information lives in the agent’s context window—its working memory. But that memory is limited, and it doesn’t carry over between sessions. And as the work gets more complex, the agent needs more information than it can hold at once.
This is exactly the same challenge you face with a real team. How do you make sure the right people have the right information at the right time? How do you prevent important details from falling through the cracks during a handoff? How do you keep everyone aligned on the objective when the project spans weeks or months?
In AI, this challenge is called context engineering, and it’s becoming a full discipline. The best AI teams in the world—Anthropic, OpenAI, Google, and independent shops like Manus—are spending more time on context engineering than on any other aspect of their systems.
Here’s how the best teams think about it, translated from their engineering jargon into concepts you already understand:
Keep your briefing document clean and consistent. When the agent’s context gets messy—conflicting information, outdated data, irrelevant details—performance degrades. The technical term is “prefix stability,” but the concept is simple: don’t change the beginning of the briefing every time. Keep the standing instructions and reference data consistent, and append new information at the end. It’s the same reason your client files have a standard structure.
The filing cabinet is the real memory. Since the model itself is amnesic, the best agents use the file system—or a database, or a document store—as their long-term memory. They write notes, save intermediate results, and create progress logs that they can read back later. It’s exactly what a good analyst does: they don’t try to remember everything. They keep notes and refer back to them. When the agent needs a piece of information that’s no longer in its working memory, it goes and reads the file.
Stay on task by restating the objective. In long-running tasks, agents lose focus for the same reason people do—the original goal gets buried under layers of accumulated detail. The best agents deal with this by periodically restating their plan. They write a to-do list at the beginning, and they update it after each step, bringing the current status back into their immediate attention. It’s the same reason good project managers start every meeting by reviewing the agenda.
Learn from mistakes instead of hiding them. When an agent makes an error—calls the wrong tool, gets a bad result, misinterprets data—the instinct is to clear the error and start over. But the best-performing agents leave the mistakes in context. Why? Because when the model sees that a particular approach failed, it shifts away from repeating that mistake. It’s like an analyst who learns from a bad trade—the lesson is more valuable than a clean track record.
The building blocks of a context engineering system can be summarized as:
Ontology: a shared vocabulary and data model. Everybody (including the AI) calls things the same thing. Your “household” is always a “household,” not sometimes a “client group” and sometimes a “family unit.”
Memory: structured mechanisms for persisting and retrieving information across sessions. Not memory in the model, but memory in the system around the model.
Tools: the agent’s ability to go get information it needs, rather than relying on what’s already in context.
Verification: mechanisms for the agent to check its own work—running calculations twice, cross-referencing sources, flagging low-confidence outputs.
Context engineering is portfolio construction for information.
Just as you’d never dump every security in the universe into a portfolio, you can’t dump every piece of information into a model’s context. You need to curate, weight, and rebalance what the model sees, optimizing for relevance, accuracy, and signal density.
The firms that build good context engineering systems will get compounding advantages. The firms that don’t will wonder why their AI tools “don’t really work.”
The Security Realities You Can’t Ignore
If you’ve read this far and you’re excited about what AI agents can do, good. Now let me pump the brakes, because the security implications of agentic AI in a regulated industry deserve blunt treatment.
