Beyond the Buzzword: Understanding Agentic AI

Over the past year, the term agentic AI has appeared everywhere, from research papers to product pages and investor pitches. Each claims to mark the next stage in artificial intelligence: systems that think, plan, and act on their own. Yet the more the word spreads, the less clear it becomes. What qualifies as agentic? How autonomous are these systems, really?

This article takes a closer look at the technology behind the buzzword, how marketing has reshaped its meaning, and what happens when one of these agents is asked to work on real tasks.

Understanding the Term “Agentic AI”

Artificial intelligence has always cycled through waves of terminology. “Machine learning,” “deep learning,” and “generative AI” each had their moment as the next great revolution. In 2025, that mantle has passed to agentic AI. The term now appears in press releases, research papers, and product pitches promising software that can “think and act.” The idea is seductive: an AI that not only answers questions but completes tasks on its own. Yet the meaning of agentic is often left undefined. The word suggests autonomy, initiative, or decision-making, but these qualities exist on a sliding scale. To understand what agentic AI really is, it helps to separate the marketing myth from the technical foundation.

Agentic AI exists on a continuum between human direction and machine initiative.

At its core, agency refers to the ability to pursue a goal through a sequence of actions. A traditional chatbot reacts to a question; an agentic system forms an objective, plans a strategy, and executes steps to achieve it. In technical terms, this process follows a loop of observe → plan → act → reflect. True agents must therefore combine reasoning, memory, and the capacity to interact with tools or data sources. Without these components, an AI might appear intelligent, but it remains reactive: essentially a sophisticated autocomplete engine waiting for human input.

In this situation, ChatGPT observed the down arrow on the button and plans to press it. It eventually presses on the arrow and reflects on the download options.

The concept of agency becomes clearer when seen as a spectrum of autonomy rather than a binary state. On one end lie reactive assistants like Siri or early Copilot, which respond only to direct prompts. In the middle are semi-agentic systems such as Copilot Code , capable of analyzing projects, proposing multi-step edits, and even writing commits, but only after explicit user approval. At the far end are agentic systems like ChatGPT Agent , which can decide which tools or connectors to use, carry out actions such as form-filling or web research, and then summarize results. Even so, these agents still operate within sandboxes and require user oversight. Each level represents not a different technology but a different balance between initiative and control.

The Meaning Beneath the Marketing

The rise of agentic AI has not only reshaped technical discussion but also marketing language. Once a niche term describing systems that plan and act, agentic has become a universal label for anything that feels proactive. Product pages now promise “agentic productivity,” “agentic creativity,” or “agentic operations,” even when the software simply automates a prompt chain. This inflation of meaning gives the impression that all modern AI is self-directed. In reality, most of these systems remain firmly under human control. The word agentic is being used aspirationally, as a way to borrow the excitement of autonomy rather than to describe it accurately.

This linguistic drift is understandable. Autonomy sells. The promise of an AI that can complete work on its own resonates with both executives and enthusiasts. Yet the gap between appearance and capability has widened. Many applications branded as “agentic” are closer to guided assistants : they generate plans or drafts, but execution still depends on user confirmation or correction. GitHub Copilot Code illustrates this middle ground perfectly: it can read a repository, outline changes, edit multiple files, and prepare commits. However, each operation requires a developer’s consent. The AI demonstrates reasoning and planning, the hallmarks of agency, but it does not act without supervision. Marketing may emphasize its independence, yet the true innovation lies in its design discipline: structured autonomy balanced by continuous human approval.

GitHub Copilot Code, now supercharged with "agent" mode.

Understanding this distinction matters because it defines how organizations and individuals should adopt these tools. Calling everything “agentic” flattens the nuance between collaboration and autonomy. True agents introduce new responsibilities: monitoring, security, and ethical oversight. Semi-agentic systems, meanwhile, can safely extend human capability without surrendering control. The difference is not academic: it shapes risk, reliability, and trust. As the term continues to spread, the challenge for technologists will be to reclaim precision in its use. Agentic AI should describe systems capable of meaningful independent action, not simply those that appear cleverer than before. The technology is evolving quickly, but clarity in language remains the best safeguard against both over-confidence and misplaced fear.

Experimenting with ChatGPT as an Agent

To move the discussion beyond theory, we decided to see what today’s most public example of agentic AI could actually accomplish. ChatGPT Agent was selected for its balance of autonomy and control. Three experiments were designed to evaluate how effectively it could handle creative, multi-step work: writing an article, adapting existing material, and producing a visual asset. Each test was observed with minimal intervention, simulating a scenario where the AI operated as independently as possible while remaining within practical limits.

The agent was able to write the article and insert the header image. However, the text did have some garbage data.

The first experiment asked ChatGPT Agent to log in to Technodabbler and write an article about agentic AI, complete with images from Unsplash. The agent began by researching the topic online, quickly gravitating toward a familiar set of sources it appeared to deem trustworthy. Once the research was complete, it prompted for login credentials, wrote the article, and attempted to insert images. This is where the system’s autonomy showed its limits. Rather than using Ghost’s built-in Unsplash integration, the agent downloaded images manually, then struggled to locate them or identify where to insert them. It eventually succeeded, though not without confusion and missing attributions. The process took about thirty minutes and required little input beyond the login step. Judged by the standards of a diligent fifth grader, the result earned a solid C: competent effort, but uneven execution.

When given an example, the agent had a much easier time writing the article.

The second experiment built on prior work. This time, the agent was tasked with using an existing draft, an article about the Fisher-Price Code-a-pillar, as a model to write a new piece on the Dash Robot by Wonder Workshop, again including Unsplash images. Because the agent was already logged in and had performed similar steps, it began confidently. Its research was more structured, and it applied the example draft effectively to replicate the format and tone. Image handling was faster, though minor formatting issues persisted. After forty-six minutes, it produced a credible article. The result lacked storytelling flow and, once again, proper image attribution, but the writing quality had clearly improved. If the first attempt showed scattered reasoning, this one displayed coherence and learning through repetition, enough for a B on the same grading scale.

When asked to create a Pin for a new article, the agent struggle to create the image, which turned out to be very generic.

For the third test, ChatGPT Agent was asked to create a Pinterest post promoting the new article. Since the system already included Canva integration, the task seemed straightforward. Instead, it highlighted the current fragility of agentic behavior in creative design. The agent began by spending fifteen minutes searching for a Canva API it could use programmatically, only to abandon the effort and request login credentials. Once in Canva, it attempted to generate a Pinterest template, but quickly ran into problems with overlapping text and misplaced elements. Its attempts to reposition content only made the layout worse. Eventually, it deleted nearly all text, leaving behind a vague image that bore no resemblance to the article’s content. The output technically met the assignment: it produced a Pinterest pin, but missed every creative and contextual goal. The performance was worth a D , saved only by the fact that a result existed at all.

Mixed Results

Watching ChatGPT Agent work was nothing short of remarkable. Even with its flaws, the level of autonomy it displayed was extraordinary: researching topics, logging into systems, navigating interfaces, and publishing content with minimal guidance. Seeing an AI reason about which buttons to press or where to insert an image felt almost surreal. It was the first tangible glimpse of software acting with intent rather than mere reaction, and that alone was worth the experiment.

Yet these tests also revealed how far the technology still has to go. It struggles with subtle design choices, narrative cohesion, and the nuance of visual communication. In its current form, an AI agent can assist the authors of this site, but it will not replace them anytime soon. The most effective use of AI remains collaborative: a partnership that mixes automation with human direction. While marketing suggests that full autonomy is the future, experience points to a different truth: the most productive agents may not be those that act alone, but those that work alongside us, amplifying our intent rather than replacing it.

Is AI helping you in your workflow, or starting to take over parts of it? How autonomous are the tools you use every day, and how much control do you still want to keep? Share your thoughts in the comments below. If you enjoyed this look at agentic AI, you might also like our exploration of how the Model Context Protocol (MCP) is enabling safer, more connected AI systems.

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