We ve been thinking about this a lot while building Traccia.
One behavior we ve seen with MCP workflows is an agent getting into repeated tool calls without making meaningful progress. The workflow hasn t necessarily failed it can just keep going.
That got us thinking about a different kind of agent monitoring: not just detecting errors, but defining boundaries on execution.
I know many countries (especially in the European Union) are moving toward requiring AI-generated content to be distinguishable from human-generated content. Gemini already does this with images, and now Claude does it with text.
Social platforms are also creating their own identifiers that can detect and label AI-generated content, such as AI labels on X and LinkedIn.
I understand all sides of this:
Governments want to prevent misinformation. AI companies can use it as a way to promote their tools. Companies and SoMe platforms can distinguish AI-generated content from human-created content. And AI tools can essentially create their own trademark on the content they generate.
Would love to hear thoughts. Here's my perspective. I'm against AI slop, but I'm not against AI. I believe AI is a great tool but it also needs greats pilots to use it well. This is what I'm working on. AI Fluency.
Biggest marker of AI fluency is AI judgement. Where and when to use AI and where and when not to.
