In press and marketing, terms like “agent-based” and “agentic” are often used interchangeably, creating confusion even among seasoned technologists. This distinction isn’t just semantic nitpicking—understanding the difference has profound implications for how we build, deploy, and govern AI systems.

Gartner has officially named agentic AI as the top strategic technology trend for 2025, predicting that “by 2028, at least 15 percent of day-to-day work decisions will be made autonomously through agentic AI, up from 0 percent in 2024” (International Banker, 2025).

With such profound shifts on the horizon, it’s critical to understand what these terms actually mean beyond the hype.

Architecture vs. Behavior: The Core Distinction

The fundamental difference is surprisingly straightforward:

• Agent-based AI refers to a system’s architecture—how it’s structured into autonomous units or modules that work together
• Agentic AI describes a system’s behavior—how it acts with agency and autonomy regardless of its underlying architecture

This distinction isn’t mere wordplay. An AI system can be agent-based without being particularly agentic (like simple multi-tool systems), and conversely, modern LLMs like Claude 3.7 and GPT-4o display impressive agentic properties while being based primarily on transformer architectures rather than fully agent-based internal designs.

The Spectrum of Agency

To clarify these concepts further, let’s examine how current leading models compare across the architecture-behavior spectrum:

What’s important to understand is that modern LLMs predominantly use transformer-based architectures internally rather than being fully agent-based in their core design. As confirmed by Wikipedia, these models are “generative pre-trained transformers” that predict the next word in sequences of text (Wikipedia, 2025).

The most comprehensive agentic capabilities we see today typically come from systems built on top of these foundation models—frameworks like AutoGPT, BabyAGI, or custom LangChain agents that add specialized modules for planning, memory, and tool use around the base LLM.

Beyond Multi-Step Prompting

A common misconception equates agentic AI with sophisticated prompt engineering. There’s a critical difference:

In multi-step prompting, you script the exact procedure: “First summarize this, then analyze it, then suggest improvements.” The AI follows your recipe precisely.

With agentic behavior, you provide a goal: “Create a winning strategy based on this article.” The AI decides what steps to take, evaluates its progress, and adapts its approach as needed—displaying genuine autonomy in the pursuit of objectives.

This distinction represents the evolution from instruction-following tools to collaborative partners capable of independent decision-making within defined parameters.

The Future: Native Agency in LLMs

The next frontier is LLMs developing native agentic capabilities without relying on external architectures. According to IBM’s recent analysis, 2025 is expected to be “the year of the agent,” with 99% of developers surveyed exploring or developing AI agents for enterprise applications (IBM, 2025). This evolution will likely involve:

1. Integrated Planning - LLMs developing internal mechanisms for autonomous decision-making and adaptation
2. Enhanced Memory - Maintaining and utilizing long-term context across interactions
3. Autonomous Tool Use - Independently selecting and employing tools without explicit guidance
4. Proactive Goal Setting - Identifying objectives based on observed patterns or unmet needs

This transition represents a fundamental shift from today’s prompt-response paradigm toward systems that can operate as independent agents capable of complex, sustained, goal-oriented behavior. As industry experts predict, while 2024 laid the groundwork for agentic AI, 2025 is expected to be the year when these technologies become truly enterprise-ready (TechTarget, 2025).

Implications for Product Leaders

Understanding this distinction has practical implications that go far beyond academic interest:

• Deployment Strategy - Agent-based architectures require different infrastructure than monolithic models, impacting everything from compute requirements to system integration approaches
• User Experience - Truly agentic systems fundamentally change how users interact with AI, shifting from instruction-giving to goal-setting and oversight
• Ethical Considerations - Agency brings new questions about autonomy, control, and responsibility that must be addressed in governance frameworks
• Competitive Advantage - Organizations that understand how to effectively harness the right balance of agency for specific use cases will outperform those taking a one-size-fits-all approach

As 2025 progresses, the line between agent-based architecture and agentic behavior will continue to blur. According to industry experts, we’ll see a shift from AI co-pilots toward more autonomous intelligent agents that take active roles in their environments (TechInformed, 2025). The companies that understand and navigate this distinction effectively will be best positioned to build AI systems that are both powerful and aligned with human needs.

Rather than asking whether your organization needs agent-based or agentic AI, the more nuanced question becomes: What level of agency do you need for specific business functions, and how should that influence your architecture and implementation choices?