Agentic Apps vs Chatbot Agents: Which Wins for Enterprise AI?
Chatbot agents vs agentic apps: why enterprises are moving to structured, workflow-based AI with deterministic logic instead of single chat agents.
6 stories
Chatbot agents vs agentic apps: why enterprises are moving to structured, workflow-based AI with deterministic logic instead of single chat agents.
AI harnesses like Claude Code and Codex control how models act. Learn the five components: context, memory, tools, verification, permissions.
Most explanations of agentic AI systems stay at a high conceptual level. In this video, I trace a single user request step-by-step through a production-level agentic AI state machine from start to finish. I break down the three core components that make AI agents deterministic and reliable: 1.
Most AI tutorials show you a chatbot demo. This video shows you how that demo actually becomes a production system inside a bank — one component at a time, with the real engineering reasoning behind each addition. We start with 3 boxes: a chat UI, an agent, and an LLM.
Most AI tutorials show you a chatbot demo. This video shows you how that demo actually becomes a production system inside a bank — one component at a time, with the real engineering reasoning behind each addition. We start with 3 boxes: a chat UI, an agent, and an LLM.
Does architecture matter more than model choice? We built the same Reddit-posting agent three different ways, ran 8 model mixes through a 14-criteria AI judge panel, and the most expensive configuration lost twice. Here is the full scoreboard.
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