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Over the past several years, technological tools in professional fields have grown at an unprecedented pace. Chief among these has been the introduction of artificial intelligence (AI), which has completely restructured several industries from the ground up.
When AI integration began in late 2022/early 2023, the mere idea of using it felt revolutionary. Now, given how much the technology has grown and how businesses’ appetite for further innovation has blossomed in tandem, there has been an even more sizable shift toward agentic AI.
Whereas initial AI tools were reactive, these new agentic AI systems are proactive, identifying opportunities and automating entire processes on their own. The strength of such tools has led many businesses to see AI differently, shifting from simple automation to long-term AI support.
Joseph Delgado, CTO at ChatRank, has been working at the intersection of AI, data, and applied systems long before the current wave of generative tools. His perspective reflects a longer arc of the technology’s evolution.
“I’ve been interested in AI before it was really part of broader commercial discussions,” Delgado explains. “Before that, it was big data, then machine learning and ranking systems. A lot of what we’re seeing now builds on those same underlying technologies.”
That continuity matters. What feels like a sudden leap forward is, in many ways, the result of years of incremental progress finally reaching a point where systems can act, not just respond.
What Makes Agentic AI Different
Agentic AI is not just about speeding up one task at a time; it is about improving the very infrastructure from which these tasks stem in the first place. While other technological tools may aim to do the equivalent of putting a fresh layer of paint on an old house, agentic AI works by replacing the house’s foundation.
Delgado frames the distinction less as a feature upgrade and more as a shift in behavior. “One of the big differentiators is the ability for these tools to run over a longer time horizon and take on more complex tasks,” he says. “They start to take on their own drive and desire to improve things.”
This is where the model breaks from traditional automation. Instead of waiting for instruction, systems begin to operate with a kind of internal momentum.
“It’s transitioning from being purely user-initiated, to systems that are already going and taking on lives of their own. They’re pulling you in when they need help,” Delgado explains.
Where prior AI tools were user-initiated, agentic AI systems surface ideas and draft outputs, prompting human review. In this way, it’s nearly a reversal of the previous AI workflow, in which users would tell the AI what to do and how to utilize its functions. Here, agentic AI frees these systems to serve as genuine co-pilots, bringing their own ideas and values to the table. As such, this system has brought about a new operating model for teams rather than just another software feature.
Use Cases: How Businesses are Using Agentic AI
Agentic AI is already being applied across fields such as engineering, sales, and marketing, where teams use AI to draft code, analyze markets, generate content ideas, and support strategic execution.
ChatRank itself emerged from a moment of disruption rather than theory.
“The problem kind of dropped itself on our lap,” Delgado says. “We had invested heavily in SEO, and then overnight, with AI overviews, we started seeing our rankings disappear.” What followed was a reframing of how visibility works in an AI-mediated internet.
“There wasn’t really a solution at the time,” Delgado adds. “And we realized this wasn’t unique to us. It was a more fundamental shift.”
Internally, the impact is already tangible. “We can kick off workflows that attempt to solve bugs and come back with a pull request,” Delgado explains. “They’re doing a lot more of the work for us, and they’re getting more intelligent.”
The same pattern extends beyond engineering. “If you had a really well-funded team mapping your market and personas, a lot of that can now be done with AI agents,” Delgado says.
This is where the conversation changes from efficiency to leverage.
Where Human Oversight Still Matters
Of course, no matter how autonomous AI becomes, it still requires discerning human oversight. This is why the aforementioned co-pilot model is generally viewed by analysts and workers alike as the best path forward for agentic AI.
These AI systems may be able to draft and recommend, but humans are still responsible for the shaping of quality, trust, and brand integrity.
The implementation of agentic AI has also prompted in-depth discussions about guardrails, narrow task definition, and review processes. All of this proves that humans are as vital as ever, just that they can now use better tools.
Despite the autonomy, Delgado is careful to draw a boundary. “We don’t want something that just runs in the background fully,” he says. “It’s meant to be part of your team. You critique it, give feedback, and improve it.”
Agentic AI as a Management Challenge, Not Just a Tech Trend
In a relatively short span of time, AI has gone from a new technical tool largely dismissed in professional circles to something numerous businesses around the globe now use daily. What’s even more surprising, though, is that the technology has continued to grow and evolve, leading to agentic AI, the next phase of AI technology.
These new systems offer potentially vast new benefits, and the long-term winners will likely be teams that combine AI initiative with human accountability, editorial judgment, and operational clarity.
