Trying something different this week. Pro subscribers are receiving a deeper dive, new article meant to support the content in the free version. Comment below or DM me to let me know what you think of this approach.
You’ve already seen how the term “AI agent” is being stretched to its breaking point. In the free edition, we defined what agents are not, and what real agentic intelligence looks like.
Now let’s take it further.
First we'll break down the architectural guts of agentic systems, show how to evaluate vendor claims, and map a path forward for teams serious about moving beyond prompts and into autonomy.
How Real AI Agents Work
(Architectural Breakdown)
You can’t identify a real agent just by the interface. Agents don’t live in the chat window. They live in their architecture.
At a minimum, an agent needs three parts:
Planner: Sets the goal and breaks it into sub-tasks
Executor: Selects and carries out actions
Memory: Maintains context, stores past actions, tracks progress
Sometimes these parts are implicit in an LLM-powered tool. Sometimes they’re explicitly built using orchestration frameworks like LangChain, n8n, CrewAI, or custom pipelines.
The point is: agents plan, act, and adapt. Without this loop, you’ve got a reasoning engine or a workflow automation. Not an agent.
In finance terms, think about an intelligent spend agent. It doesn’t just answer “What were our top vendors last quarter?”
It:
Analyzes vendor trends
Detects pricing outliers
Flags potential overspending
Suggests changes
Sends recommendations to the appropriate budget owner
And it does this continuously, without waiting for a prompt.
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