An engineer turned developer built a chatbot inside gvSIG desktop that tapped the Gemini API. By giving the bot access to the app’s tables and view state, it could generate charts and run queries. The project sparked curiosity about what truly makes an AI system an agent today.

While reading about large language models, the author noticed a flood of videos labeling tools like Claude Code and Copilot as the next generation of agents. Yet, when the same tools were examined, they behaved only as task‑oriented assistants, lacking true autonomy or proactive sensing in real world.

The confusion stems from the term’s over‑extension; classic definitions from Russell and Norvig describe any system that perceives and acts. Modern LLM‑powered tools fit that bill, but the real distinction lies between task executors—reactive, instruction‑driven—and proactive agents that autonomously respond to environmental changes without human intervention today again.

Recognizing this taxonomy clarifies expectations and prevents hype‑driven disappointment. Developers can now label a Gemini‑CLI‑based assistant as a sophisticated task executor rather than a fully autonomous agent. Future research must focus on building true proactivity, integrating continuous sensing and goal‑driven decision making into LLM frameworks for the next.