Most discussions around AI agents still end up being about frameworks and models. LangGraph vs CrewAI, which model has better tool calling, prompt engineering, that sort of thing. But I don’t think building agents is the hard part anymore. The tooling has improved so much over the last year that getting an agent working isn’t nearly as intimidating as it used to be. What’s starting to matter more is everything that comes after. How do you deploy updates without breaking something? How do you test changes before they reach production? How do you keep track of which version is running where? What does rollback look like? How are permissions, approvals, and audit logs handled when you have multiple agents doing different jobs? Software engineering eventually settled on pretty standard ways of handling all of this. With AI agents, it still seems like every team is piecing together its own solution. So I’m wondering what people are actually doing today. Are most teams building their own internal tooling around the framework they’ve chosen? Is there already a category of tools solving these problems that just doesn’t get talked about as much? Or is this still one of the biggest gaps in the ecosystem? submitted by /u/Financial_Ad_7297
Originally posted by u/Financial_Ad_7297 on r/ArtificialInteligence