Why Your AI Agent Isn’t Really an Agent (And Why That’s Okay)

The term "AI agent" has become a marketing darling, applied liberally to everything from chatbots to task automation tools. Yet, most of these systems fall short of being true agents. They may perform complex tasks or integrate impressive technologies, but they lack the full set of capabilities required to claim the title. Instead, what we often see are agent-like or agentic systems. They're still powerful, but not quite the real deal. And that’s perfectly fine, as long as we’re clear about the distinction.

Defining a True AI Agent

To understand why many systems aren’t true agents, we need a solid definition. A true AI agent is an intelligent system designed to autonomously achieve a goal by observing its environment, reasoning about what actions to take, and executing those actions using tools at its disposal. It operates with minimal human intervention and adapts dynamically to new information or changing circumstances.

A true agent has three essential characteristics:

1. Autonomy: It acts independently, making decisions without needing constant human input or predefined scripts.
2. Adaptability: It learns and improves over time, refining its actions based on past experiences and new data.
3. Proactivity: It doesn’t wait to be told what to do; it anticipates needs, sets sub-goals, and takes initiative to achieve its objectives.

Why Most AI "Agents" Don’t Qualify

Despite impressive capabilities, most systems marketed as AI agents lack one or more of these core traits. Here’s why your "AI agent" might not be a true agent:

1. Reactive, Not Proactive

Many so-called agents are reactive systems. They respond to user prompts or pre-set triggers but don’t anticipate needs or take initiative. A chatbot that provides answers to FAQs or a scheduling assistant that responds to calendar requests falls into this category. True proactivity, where the system identifies goals and works toward them independently, is rare.

2. Limited Adaptability

Some systems can learn within narrow contexts, like improving customer service responses based on feedback. However, they often lack the ability to generalise or adapt across domains. A true agent would adjust its behaviour dynamically, refining its approach as it encounters new environments or challenges.

3. Tool Use Without True Reasoning

Modern systems often integrate with APIs, databases, and external tools, but many lack the cognitive architecture to decide when and how to use those tools effectively. For example, a system that retrieves data from an API based on user input isn’t reasoning, it’s following predefined instructions.

4. Domain-Specific Expertise

Most AI systems excel in specific, narrow applications, like processing invoices or recommending products. True agents, however, can generalise and operate effectively across multiple domains.

Agent-Like vs. True Agents

While these systems fall short of the true agent definition, they can still exhibit agent-like or agentic characteristics:

• Agent-Like Systems: These systems simulate certain aspects of agency, like autonomy or tool use, but only within tightly defined boundaries. For instance, a customer service bot may independently escalate complex issues, but it doesn’t make decisions beyond its pre-programmed domain.
• Agentic Behaviour: Even within limited contexts, systems can display behaviours resembling true agency, such as reasoning through a complex query or dynamically choosing the right tool for a task.

Using terms like agent-like or agentic is both accurate and honest. It acknowledges the capabilities of the system while avoiding the overreach of calling it a full-fledged agent.

Why It’s Fine to Be Agent-Like

It’s tempting to think of agent-like systems as "less than," but they are incredibly valuable in their own right. Here’s why:

1. They Solve Real Problems: Agent-like systems can automate repetitive tasks, enhance workflows, and provide dynamic interactions. These solutions deliver tangible business value, even if they aren’t fully autonomous.
2. They’re Easier to Deploy: True agents, with their need for advanced reasoning and autonomy, require sophisticated architectures that are costly and complex to build. Agent-like systems, by contrast, can often achieve impressive results with simpler implementations.
3. They Pave the Way for True Agents: The incremental progress made by agent-like systems helps refine the technologies, frameworks, and methodologies needed for fully autonomous agents.
4. They’re Honest About Limitations: Adopting the term "agent-like" sets realistic expectations. It builds trust with users and stakeholders by delivering what’s promised, rather than overhyping capabilities.

The Road to True Agents

The journey toward true agents is ongoing, but it requires overcoming significant challenges:

• Advanced Orchestration Layers: True agents need robust cognitive architectures that combine reasoning, planning, and execution seamlessly.
• Dynamic Tool Integration: Agents must learn to select and use tools effectively, adapting to new tools and tasks as needed.
• Generalisation Across Domains: Moving from narrow expertise to broader adaptability is a key milestone for true agents.

While progress is being made, most systems today are still in the agent-like category. This is not a limitation. It’s a step toward a more ambitious goal.

Conclusion

Calling something an "AI agent" implies a high standard: autonomy, adaptability, and proactivity. Many of today’s systems fall short of this definition, but that doesn’t diminish their value. By embracing terms like agent-like or agentic, we can set honest expectations while still celebrating the capabilities of these systems.

The truth is, we don’t need every system to be a true agent. Sometimes, an agent-like solution is exactly what’s needed: a specialised, focused tool that gets the job done.

Let’s give credit where it’s due, while staying grounded in what’s real. After all, not every robot has to be a superhero.