While agentic AI systems aim to autonomously set and achieve goals, practical limitations such as API access and system complexity reveal significant hurdles, highlighting the gap between the technology’s promise and current capabilities.
Agentic AI promises something more ambitious than a chatbot or an automated workflow. In theory, it is a system that can receive a goal, choose tools, make decisions and take actions with limited human input. Kobi Toueg’s Medium essay uses that idea to imagine an agent tasked with a simple business objective: making $1,000 on Medium. The thought experiment is appealing, but the article argues that the first obstacle is practical: Medium does not provide a public API for this kind of automation, and scraping the site would be brittle, difficult to maintain and ill-suited to a system that depends on repeated feedback loops.
That technical constraint reflects a broader pattern in agentic design. According to TechTarget, agentic systems are built around perception, decision-making and action execution, with the agent moving from one step to the next in pursuit of a goal. IBM describes the same architecture as a departure from non-agentic software, because the system is expected to act with some autonomy rather than wait for constant human instruction. In practice, that means an agent is not just a language model with a prompt; it is an orchestrated stack of planning, memory, tools and control logic.
The promise of that stack is also what makes it difficult to build well. Multiple explainers on agentic architecture, including those from Agentic AI Masters, CrossML, UpGrad and DigitalAPI, point to the same recurring problems: growing system complexity, fragmented data, fragile orchestration, weak API readiness and the risk of optimising for technology rather than a clearly defined business problem. As those components multiply, debugging becomes harder and maintaining alignment between the agent’s actions and the original objective becomes more precarious.
Toueg’s central argument is that the idea of a self-directed writer optimising for revenue exposes both the appeal and the unease of agentic AI. The system may be technically imaginable, but it depends on access, control and trust that do not yet exist in a clean, reliable form. That leaves agentic AI suspended between a compelling product vision and a set of unresolved engineering and ethical trade-offs.
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The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
10
Notes:
The article was published on April 30, 2026, and does not appear to be recycled or republished content. No earlier versions with differing figures, dates, or quotes were found. The narrative is original and timely.
Quotes check
Score:
10
Notes:
The article does not contain direct quotes. The content is original and does not reuse quotes from other sources.
Source reliability
Score:
7
Notes:
The article is published on Medium under ‘The Thoughtful Engineer’ publication, which is a personal blog. While Medium hosts a variety of content, the lack of a formal editorial process raises concerns about the reliability and credibility of the source. The author’s credentials and expertise are not clearly established, which further diminishes the source’s reliability.
Plausibility check
Score:
8
Notes:
The article presents a theoretical exploration of agentic AI systems and their challenges. The claims made are plausible within the context of current AI capabilities and limitations. However, the lack of supporting evidence or references to reputable sources makes it difficult to fully verify the accuracy of the claims. The absence of specific examples or case studies further limits the assessment of plausibility.
Overall assessment
Verdict (FAIL, OPEN, PASS): FAIL
Confidence (LOW, MEDIUM, HIGH): MEDIUM
Summary:
The article presents an original and timely exploration of agentic AI systems. However, it is published on Medium under a personal blog without clear editorial oversight, and lacks citations or references to independent, reputable sources. The absence of direct quotes and the reliance on the author’s analysis without external verification sources further diminish the credibility of the content. Given these concerns, the article does not meet the necessary standards for publication under our editorial indemnity.
