Unless another major agent use case takes off, I expect hiring for generic agent harness work to cool over the next six months. By harness, I mean the software around the model: tool execution, memory, retries, permissions, and the loop that keeps a task moving. That work matters. But how many companies need their own version built from zero? If reusable systems handle more of the basics, a team can spend less time assembling an agent and more time fixing the actual business process. The hiring premium for “I can build your general-purpose agent framework” could shrink pretty quickly. I’d expect demand to move toward evaluations, integrations, failure recovery, and domain knowledge. Someone who can make an agent reliably process messy invoices has a clearer business case than someone building another generic orchestration layer. The obvious counterargument is that adoption could grow fast enough to create more jobs overall. I’m predicting a squeeze on one kind of role, not the disappearance of agent engineering. For people hiring or building agents: which parts still justify a dedicated team, and which are becoming standard infrastructure? submitted by /u/yi111
Originally posted by u/yi111 on r/ArtificialInteligence