Why AI Agents Matter (Part 1 of 2): A Perspective for Business and Technology Leaders

AI agents, agentic AI, or agentic systems—whatever you call them—are making waves in technology discussions. But much of the conversation so far has been aimed at developers or architects. Often, it includes examples of an AI agent replacing a travel agent, invariably booking a trip to Tahoe. While entertaining, this anthropomorphized example doesn’t capture the full significance of AI agents for executives or technology leaders. Why? It fails to provide a holistic view of how AI agents can transform their software landscape or fit into it.

Six years ago, I was fortunate to work on a Fellows project at Gartner. My topic was the future of organizations and how digital technology would radically change the concept of an organization. To explore this, I focused on identifying “inevitable truths”—future scenarios with a high likelihood of emerging based on proven trajectories (e.g., computers becoming faster, cheaper, and smarter). Of the ideas I developed, two—“automated commercial agents” and “invisible shape-shifters” (admittedly not the best names)—closely align with what we now call AI agents. Even then, it was clear that technology like this would evolve, as it satisfied key needs for exploiting AI’s full potential.

Here’s the bottom line: AI agents represent a shift that makes software more active, adaptive, and intelligent. Note that we are in the very early days—the possibilities of AI agents are easy to imagine but hard to realize today. For executives, the question isn’t only whether to invest in AI agents now but how to make investments today that won’t become irrelevant as AI agents take hold over the next few years.

AI agents unlock three transformative capabilities:

1. Turning AI tools like generative AI (genAI) and large language models (LLMs) from passive participants into active players in commercial and other digital interactions.
2. Combining cutting-edge AI and traditional software into a powerful alliance for uncovering new or better value.
3. Making software systems more flexible and nuanced, mirroring the real world and expanding their scope.

Let’s explore why this matters.

What Are AI Agents?

At their core, AI agents are software systems that use artificial intelligence to achieve objectives. They can break tasks into subtasks, make decisions, and use tools or data to get things done—all while learning and optimizing over time.

It’s important to note that their autonomy isn’t absolute. Decisions around course of action, tool selection, result validation, optimization, and self-improvement often include scaffolding and controls for safety, reliability, and performance.

While some capabilities of AI agents are seen in Robotic Process Automation (RPA), AI agents represent a step change with dynamic learning and optimization capabilities. However, compared to RPA, they are still much more nascent.

Why AI Agents Are Important?

1. Turning Generative AI from Passive to Active Entities Today, generative AI and LLMs are largely passive participants. They’re either accessed through simple interfaces or used within rigid workflows. AI agents elevate them into powerful, independent entities capable of initiating, managing, and optimizing tasks.

This shift unlocks:

• Incremental Improvements: Improving accuracy and handling greater variability in automated tasks like scheduling resources or processing applications.
• Large-Scale Automation: A system that automates significant aspects of your supply chain—monitoring stock, ordering materials, and negotiating prices with much less human oversight, but still requiring oversight.
• Entirely New Possibilities: AI agents managing marketing campaigns with hundreds of A/B tests across multiple segments and social media channels.

This leap from passive assistance to active involvement expands the boundaries of what AI can do in the digital world.

2. Combining Cutting-Edge AI and Traditional Software into a Powerful Alliance AI agents act as a bridge between advanced AI systems and pre-AI software ecosystems. Traditional systems process specific inputs with fixed structures, while LLMs and genAI excel at natural language interactions (like how we interact with ChatGPT).

AI agents mediate between these worlds, enabling generative AI systems to interact with legacy software, databases, and the web. This allows organizations to extract greater value from their existing systems without requiring massive new investments.

3. Making Software More Real-World-Ready Today’s software systems excel at rigid, deterministic processes. But real-world decisions are rarely black and white—they’re nuanced, probabilistic, and involve trade-offs.

For instance:

• Critical Business Decisions: A CEO considering a billion-dollar investment must balance financial data with broader market dynamics and stakeholder sentiment.
• Personal Choices: A family deciding whether to buy their first home weighs affordability alongside intangible factors like neighborhood appeal.

AI agents can represent objectives and guide decisions in these scenarios, blending data-driven insights with flexibility and incorporating previously unused data sources. Over time, this capability could reshape how we approach both professional and personal decision-making.

Closing Thoughts

AI agents mark a critical evolution in how we use digital systems. By turning AI from passive tools into active collaborators, bridging advanced and legacy technologies, and introducing more flexibility and nuance to software, they open new frontiers for innovation and problem-solving.

In the next article, I’ll explore the business implications of this phenomenon, discussing how companies can adapt and thrive in this rapidly changing landscape.