Stop Calling Everything an AI Agent
Why lazy language is killing real innovation in finance
“Let every eye negotiate for itself and trust no agent.”
~ William Shakespeare, Much Ado About Nothing
If only software vendors took that advice.
Welcome to the modern tech demo, where EVERYTHING is an agent.
Your chatbot? Agent.
Your invoice automation? Agent.
Your spreadsheet plugin that fills in last month’s forecast? Somehow, also an agent.
But when every tool gets labeled this way, the word loses all meaning.
That’s a problem.
Because when real agentic AI (systems that can plan, adapt, and act on their own) arrives, most finance teams won’t be able to spot it. They'll think it's just another minor upgrade to the same old tools.
Misunderstanding the nature of these tools causes teams to make poor investments. They spend on shallow features while neglecting the deeper infrastructure required for real transformation.
Take away the marketing language, and today’s finance tools mostly fall into three buckets:
Chatbots that answer prompts
Assistants that help when asked
Automations that follow set scripts
Even today’s most capable models, like ChatGPT o3, aren’t true agents. They can reason, simulate initiative, and explain complex ideas, but they still rely on external prompts and guidance. They don’t perceive their environment or act independently toward goals.
In short, they lack autonomy.
Why the Distinction Matters
Andrew Ng notes that real agents are not just standalone bots. They are structured workflows that plan, reflect, and take action across tools. His point is essential: agents are defined by their architecture, not their interface.
To cut through the hype, we need a better definition of progress in AI.
OpenAI’s five-stage roadmap to superintelligence is a helpful reference:
Chatbots that respond to inputs
Reasoners that analyze, explain, and solve complex problems
Agents that pursue goals independently
Innovators that help invent entirely new ideas and tools
Organizational AI that performs the work of entire companies through planning, coordination, and execution
Most finance tools today sit somewhere between stage two and stage three. They use reasoning models or predefined workflows with limited autonomy. But many vendors describe them with language that suggests much more. That disconnect creates confusion, wastes budget, and slows real adoption.
What Real Agents Look Like
Think of your best FP&A analyst. They don’t just follow instructions, they:
Spot anomalies
Investigate root causes
Recommend strategic adjustments
Follow through to implementation
That kind of proactive, adaptive behavior is what a true agent should replicate.
Real agents do four things well:
Initiate action toward specific goals
Adjust to new information
Select from multiple options
Learn from outcomes to improve next time
At their core, agents run a continuous loop:
Whether powered by logic or learning models, this architecture is consistent. True agents act with intent, not just response.
The Risk of Getting It Wrong
Calling a chatbot an agent might seem harmless. But the risks are real:
Teams skip building oversight, assuming autonomy is already built in
Users expect too much and become frustrated when tools fall short
Finance leaders overlook truly innovative tools because they seem like more of the same
I flagged these concerns in 2023 while writing about AI finance assistants. The situation hasn’t improved. We still need systems that understand the structure of financial data, the constraints of regulation, and the context of decision-making.
Coming Up in the Pro Edition
We’ll go deeper with:
A walkthrough of agentic workflows in finance
Prompts to evaluate whether your vendors are selling reasoning or autonomy
A governance checklist you can implement now
Scenarios to prepare your org for real agent deployment