Escrito por: Maisa Publicado: 15/04/2025
We keep hearing the term “AI Agents” everywhere these days. LinkedIn is flooded with posts about them. Tech conferences can’t stop talking about them. Every startup pitch seems to include them.
And yet, when we ask ten different people what an “AI Agent” actually is, we get ten completely different answers.
This isn’t merely semantics. It’s a problem with deep historical roots. The concept of “agency” has challenged thinkers since ancient times. Aristotle explored it through his notion of “efficient cause”, what initiates action. Plato examined it through questions of intention and purpose. The Stoics considered the relationship between individual action and natural order.
These ancient philosophers recognized something we’re rediscovering today: defining agency is inherently complex. It touches on intention, autonomy, purpose, and responsibility, concepts that resist simple definition.
Let’s be candid: the term “AI Agent” has become almost meaningless. Some vendors apply the label to sophisticated chatbots. Others use it for basic automation with a thin AI veneer. The enthusiasm has outpaced the clarity.
We’ve sat through countless demos where the presenter uses “autonomous agent” language, but what they’re showing is just a rigid workflow with some LLM responses inserted at key points.
Meanwhile, back in the real world, teams are drowning in mundane tasks. How many hours did your organization lose last month to spreadsheet updates? Data entry? Report generation? Following up on routine processes? Likely more than anyone would prefer to acknowledge.
All that time could have been invested in the work that actually moves the needle: creative problem-solving, relationship building and strategic thinking.
When we talk about a true AI Agent, we’re talking about something specific: a system where the AI model itself (usually an LLM) actively manages its own workflow. It makes decisions about what to do next based on the current situation, not just following pre-programmed steps.
It’s like the difference between a simple calculator and a financial advisor. The calculator performs operations you explicitly request, while the advisor analyzes your situation, considers multiple factors, and recommends appropriate actions to achieve your goals.
What’s commonly marketed as “agents” today are often just “LLMs with tool access.” This reductive approach is like defining a human as “a body with hands.” Such simplistic definitions miss the essence of true agency: independent judgment, adaptation, and purposeful action. A genuine AI agent needs to be much more than just an LLM that can call functions.
Here’s what concerns us most: as companies rush to deploy these so-called “agents,” we’re creating an accountability vacuum.
We recently spoke with a banking executive who perfectly summed up the problem: “I can’t hand over loan decisions to a system that can’t explain itself.”
She’s absolutely right. In business, we need systems that are:
We wouldn’t trust our mortgage approval to a black-box AI. Or our insurance claim. Or a critical research direction for our company. Some decisions are too important for opaque algorithms.
This is exactly why we’ve become so passionate about the concept of Digital Workers. They cut through the ambiguity and address the core challenge.
We value Digital Workers because they come with clarity built in. They’re purpose-built for specific business functions. They have well-defined scopes and goals. When you deploy one, you know exactly what it’s supposed to do and how it will approach its work.
It’s like the difference between hiring a vague “consultant” versus a specialized professional with clear expertise and responsibilities.
After working with different AI approaches for years, we’ve observed what sets genuine Digital Workers apart:
Every decision, every action, every piece of data accessed, it’s all logged and traceable. When something goes wrong (and something always does eventually), you can see exactly what happened and why. No more mysterious “the algorithm did it” explanations.
Digital Workers aren’t disruptive forces that demand everyone else change to accommodate them. They understand organizational boundaries and protocols. They know when to ask for help, when to escalate issues, and how to keep the right people informed at the right time.
We’re continually impressed by how Digital Workers improve with each task they complete. They capture the institutional knowledge that usually walks out the door at 5pm. They refine their processes based on feedback, becoming more valuable team members over time.
Business conditions change constantly. Forms get updated. Processes evolve. Exceptions arise. Digital Workers can adapt to these changes without needing to be completely reprogrammed. They maintain their effectiveness even as the environment shifts around them.
The best Digital Workers we’ve seen can handle the messy reality of business data, parsing emails, extracting information from documents, working with databases and spreadsheets. They bridge the gaps between systems that never talk to each other.
We’re ready to move beyond the ambiguity in AI marketing. The Digital Worker approach feels refreshing because it prioritizes what actually matters in business: clear responsibilities, transparent operations, and measurable results.
In our experience, the businesses getting the most value from AI aren’t chasing the latest buzzwords. They’re deploying Digital Workers with specific responsibilities and clear accountability mechanisms.
As AI becomes more embedded in our daily work, the quality of our relationship with these systems will become increasingly important.
The most successful implementations we’ve seen treat Digital Workers as team members with specific tasks, not magical cure-alls. They establish clear expectations, provide feedback, and hold the systems accountable for results, just like they would with human team members.
In a world where AI terminology often obscures more than it reveals, this grounded approach delivers genuine value. And ultimately, that’s what matters most.
Author: David Villalón