AI Terminology and Capability Distinctions
The discussion first surfaced on 26 Mar, when ArtificialInteligence asked where a prompt chain ends and a “true AI agent” begins. By 30 May, the focus had moved to customer support, with another ArtificialInteligence discussion asking what separates a chatbot from so-called agentic AI. On 22 Jun, the debate widened to AI terms in common use and what they mean in practice. Dev.to followed on 01 Jul with a direct comparison of AI agents and chatbots.
Industry voices show why the labels remain slippery. ChatGPT offered the formulation that generative AI creates while agentic AI executes. It also described replacing a $600 jewellery shoot with AI tools, including the workflow, what worked and the one detail that still gave it away. ArtificialInteligence framed Google’s next AI bet as agents that do the work, examined how useful agents are today and how much remains hype, and observed that some products are starting to optimize humans out of the workflow entirely. Another ArtificialInteligence post claimed that 90% of products called “AI Agents” are RAG wrappers.
LocalLLaMA supplied the commercial version of the same uncertainty through someone hoping to leave big tech and sell AI agents to small businesses, while asking where to begin learning. OpenAI pointed to problems with AI/API calls and argued that only LLM companies can fix them. Four signals later, the industry still has several labels chasing capabilities that need to be demonstrated in use.
Taking this as factual, impact is 10%. Developers, support teams and small businesses are affected because product descriptions shape purchasing decisions and technical expectations. The immediate change is mostly vocabulary: chatbot, prompt chain, RAG wrapper and agent need clearer definitions tied to what a system can execute without human intervention.
Watch for vendors to publish precise capability boundaries, especially around tool use, API calls, prompt chains and independent execution. Evidence that products reliably remove human steps or fail to do so would sharpen the distinction more quickly. Another round of naming exercises would provide less clarity.
<!-- SC_OFF --><div class="md"><p>Gemini 3.5 Flash launched Tuesday, and Google barely framed it as a conversational product. Instead, the company focused on coding pipelines,
<!-- SC_OFF --><div class="md"><p><strong>quick context:</strong> we're a small AI creative agency, 9 people, based in Pune. we work with brands on commercial visuals product
<!-- SC_OFF --><div class="md"><p>I'll be upfront about my endgame: I work at a large tech company, I have a niche picked out, and I'm making the move to build and sell AI agents
<!-- SC_OFF --><div class="md"><p>I want to believe in agents, but I am stuck in this gap between a cool concept and daily reality. On social media it is always these perfect
<!-- SC_OFF --><div class="md"><p>I think we're entering a strange phase where software is no longer helping users do tasks better, it's trying to remove the user from the task
<!-- SC_OFF --><div class="md"><p>A real agent needs autonomy, planning, and actual tool use.</p> <p>If your "agent" just searches a PDF, summarizes it, and spits it
<!-- SC_OFF --><div class="md"><p>We are moving past AI that just answers prompts to autonomous systems that can break down goals, use APIs, and complete complex workflows on
<!-- SC_OFF --><div class="md"><p>With the recent rise of MCP tools, I’ve been seeing more discussions about how people doing research or data scraping can now get usable datasets
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