Agent Washing: 5 Code-Level Tests to Tell Real AI Agents from Fakes

A RAND study found that 80-90% of AI agent projects fail. But here is the part nobody talks about: many of those projects were never agents to begin with. Welcome to agent washing -- the practice of slapping the word "agent" onto any product that calls an LLM. Chatbots become "conversational agents." Cron jobs with GPT become "autonomous agents." A single API call wrapped in a while True loop becomes an "agentic workflow." The term has been making rounds in enterprise circles -- Forbes , Gartner, and Reddit's r/AI_Agents have all flagged it. But most of the coverage targets procurement teams and enterprise buyers. Nobody has written the developer version: concrete, code-level tests you can run against your own tools. This article fixes that. Five Python test functions. Run them against anything claiming to be an "AI agent." The results will tell you more than any vendor demo. What Makes Something an Agent (Not Marketing) Before the tests, we need a working definition. Not the marketing