As artificial intelligence continues to mature, we’re seeing a shift from narrowly focused, task-based systems toward something far more dynamic: agentic AI. While traditional AI has revolutionized everything from customer service to fraud detection, agentic AI introduces a fundamentally new paradigm—one where machines act with greater autonomy, initiative, and purpose.
So what exactly is agentic AI, and how does it differ from traditional approaches? Let’s explore.
Traditional AI refers to systems designed to perform a single, well-defined task. These systems rely on machine learning models or rule-based algorithms to make decisions, but only within the scope of the specific problem they’ve been trained or programmed to solve.
Examples include:
While incredibly effective in these contexts, traditional AI systems are reactive. They don’t think ahead, don’t have goals, and can’t act outside their narrow domain. You give them an input, they return an output—that’s it. They don’t plan, adjust, or take initiative.
Agentic AI, on the other hand, describes systems that function more like autonomous digital agents. These systems aren’t just responding to inputs—they’re setting and pursuing goals, making decisions, adapting to feedback, and even using tools to complete multi-step tasks.
In short, agentic AI systems are:
One of the earliest glimpses of this concept came with tools like AutoGPT and BabyAGI—experimental frameworks built on top of large language models like GPT-4. You could give them a goal like "research the best marketing automation tools," and they would plan the task, conduct research, summarize findings, and deliver results.
More recently, Devin, an AI software engineer developed by Cognition, has pushed this even further. Devin can read a software spec, write code, fix bugs, test deployments, and even interact with GitHub—all autonomously.
These AI agents behave more like junior employees or virtual assistants, capable of managing projects and following through on goals over time.
| Feature | Traditional AI | Agentic AI |
| Autonomy | Low – executes predefined tasks | High – makes decisions and takes initiative |
| Scope | Narrow – one task at a time | Broad – multi-step workflows and planning |
| Initiative | Reactive – responds to prompts | Proactive – takes action based on objectives |
| Memory/Reasoning | Often stateless or limited | Can remember, reason, and refine strategies |
| Examples | Spam filters, recommendation engines | AutoGPT, Devin, AI research assistants |
The transition from traditional to agentic AI represents a leap toward more general-purpose and adaptive systems. These agents have the potential to:
But with greater autonomy comes greater complexity—and risk. Agentic AI introduces new challenges around trust, safety, alignment, and transparency. These systems must be carefully monitored to ensure they act in ways that are beneficial, ethical, and aligned with human values.
Baked into the 7SIGNAL platform, EYERIS AI uses agentic principles to provide immense value across the organization. EYERIS combs through massive amounts of network and device experience data to glean insights into enterprise connectivity, tailor recommendations to your business, and guide teammates through the steps to implementation. This learning, evolving, proactive agent empowers IT teams to work smarter and ensure network continuity, no matter the scenario.
Let our team walk you through EYERIS’ capabilities and roadmap by booking a demo.
Agentic AI is not just an upgrade—it’s a rethinking of what AI is capable of. By moving beyond task-based intelligence, we unlock the potential for machines that work alongside us, not just for us. As this new generation of AI continues to emerge, understanding the difference between traditional and agentic systems will be essential for businesses, developers, and society as a whole.