The world of AI is moving fast. Every day, new tools emerge claiming to be "agents" that will revolutionize how we work, communicate, and live. But here’s the truth: most of these so-called agents are not agents at all.
To understand why, let’s start with a question: what do you expect from an agent in the real world? Picture a travel agent. You don’t call them up expecting a list of suggestions that leave you to figure out the details. You want action. You want them to book your flights, arrange your accommodations, and ensure your itinerary is flawless.
True agents don’t just provide information—they take responsibility, act on your behalf, and solve your problems. Yet, in the AI landscape, the term "agent" is often misapplied to systems that do little more than respond to commands or provide suggestions.
It’s time to clarify what an agent is, why most AI systems fall short, and what’s needed to create true agentic AI that can act, adapt, and integrate meaningfully into our lives.
What Is an Agent? The Travel Agent Analogy
Let’s break it down using the travel agent analogy. Imagine you’re planning a trip. You approach three types of "agents":
The Informational Agent:
You ask about travel destinations, and they hand you a pamphlet with popular options. No follow-up, no action. It’s entirely up to you to book and organize everything.The Suggestive Agent:
This one listens to your preferences and gives you tailored recommendations. They might even highlight special deals. But again, they stop short of taking action.The Actionable Agent:
Now we’re talking. This agent understands your needs, suggests options, and takes care of everything: flights, hotels, car rentals, even travel insurance. They follow up to ensure everything runs smoothly.
In this analogy, most AI systems today fall into the first two categories. They’re either informational or suggestive, capable of providing answers or ideas but unable to act meaningfully on your behalf. What we need are systems that can act like the third agent—true agents that take action, solve problems, and manage tasks autonomously.
The Misuse of "Agent" in AI
In the rush to market new products, the term "agent" has been applied liberally—and incorrectly. Systems labeled as "agents" often lack the core qualities that define one.
Here’s what’s missing:
Actionability:
True agents don’t just respond; they execute. A chatbot that provides a recipe when you ask isn’t an agent. An AI that orders your groceries based on that recipe and your pantry inventory? Now that’s an agent.Autonomy:
Agents operate with a degree of independence. They don’t require micromanagement for every small task. They understand context and make decisions within their domain of expertise.Integration:
Real agents are connected to the systems and data they need to perform their roles. A travel agent without access to booking systems is just a well-informed friend. Similarly, an AI without integration into your calendars, email, or business tools can’t be a true agent.
Calling an AI system an "agent" when it lacks these qualities not only dilutes the term but also misleads users about what’s possible.
What True AI Agents Require
To build AI that functions like a travel agent—an intelligent system that can act on your behalf—we need three critical components:
Access to Data and Systems:
Without access to your calendars, emails, CRMs, or third-party tools, AI systems are stuck in suggestion mode. Access unlocks the ability to act.Secure, Open Integration:
Integration is key, but it needs to be done responsibly. Open protocols like the Model Context Protocol (MCP) offer a way forward, enabling AI to connect securely with diverse systems while maintaining user control.Proactive Intelligence:
True agents don’t just wait for commands. They anticipate needs and act. For example, a travel AI should notify you about flight delays and rebook your itinerary automatically.
Without these elements, an AI system is simply a tool—not an agent.
MCP: The Missing Link for True Agentic AI
This is where the Model Context Protocol (MCP) comes into play. Think of MCP as the universal connector for AI systems. Just like HTTP standardized how websites communicate, MCP provides a shared framework for AI to integrate with external systems, enabling the kind of access and actionability that agents require.
Why MCP Is Revolutionary
It Enables Integration:
MCP allows AI systems to access tools and data across platforms, whether it’s Microsoft 365, Salesforce, or bespoke internal systems. This is the foundation for actionability.It Prioritizes Security:
With built-in user controls and privacy measures, MCP ensures that data is accessed responsibly. You don’t need to sacrifice security for functionality.It Encourages Innovation:
As an open standard, MCP empowers developers worldwide to create integrations and expand what’s possible. No single company controls it, which means faster innovation and broader adoption.
How MCP Brings the Travel Agent Analogy to Life
Let’s revisit the travel agent example. Imagine an AI travel agent powered by MCP:
- It connects to your email to find flight confirmation details.
- It accesses your calendar to schedule accommodations and activities.
- It integrates with booking platforms to reserve your hotel and car rental.
- It notifies you about delays and adjusts your plans in real time.
This kind of seamless orchestration isn’t just theoretical—it’s achievable with the right protocols in place.
What Happens Without MCP?
Without open standards like MCP, the future of AI risks stagnation. Here’s why:
Siloed Systems:
Closed ecosystems force users to jump between tools, manually transferring data. AI can’t act meaningfully when locked inside one platform.Limited Innovation:
Proprietary systems restrict what developers can build, slowing the pace of progress.User Frustration:
Imagine asking your "AI agent" to book a meeting and being told it can’t access your calendar because it’s on a different platform. That’s not the future anyone wants.
Setting the Record Straight: What an AI Agent Should Be
It’s time to stop calling everything an "agent" and reserve the term for systems that truly embody its meaning. A true agent must:
- Act: Execute tasks autonomously, not just suggest options.
- Integrate: Seamlessly connect with the systems and data needed to perform its role.
- Adapt: Operate intelligently within context, making decisions without constant input.
Anything less is a tool, not an agent.
A Vision for the Future
Imagine a world where true AI agents are commonplace:
- Your personal AI manages your day, rescheduling meetings and notifying team members without you lifting a finger.
- Your business AI automates workflows, connecting CRM systems with project management tools to ensure nothing falls through the cracks.
- Your household AI monitors your grocery inventory, orders supplies, and schedules deliveries—all without being asked.
This isn’t just possible—it’s inevitable if we embrace open standards like MCP and demand true agentic capabilities from our AI systems.
The Call to Action
The misuse of the term "agent" isn’t just a marketing issue; it’s a barrier to understanding and building the future of AI. Here’s how we can change that:
- Clarify the Definition: Let’s reserve "agent" for systems that truly act, integrate, and adapt.
- Adopt Open Standards: Developers and businesses need to rally behind protocols like MCP to enable seamless integration.
- Demand Better Tools: As users, we should hold AI developers accountable for delivering systems that live up to the promise of agency.
Conclusion: Agents, Not Just Tools
The potential of AI lies not in chatbots or suggestive tools but in true agents that take action on our behalf. To get there, we must align on what an agent really means and invest in the technologies—like MCP—that make it possible.
Let’s stop misusing the term and start building systems that live up to its promise. True agents don’t just assist—they transform how we work and live.