Deploying in production, the current agentic systems do not really work well. Workflow automation does. The reason is very native to LLMs, but also incredibly basic. Every agentic system starts with planning and reasoning module, where an LLM evaluates the task given and plans about how to accomplish that task, before moving on to next steps.
When an agent is given a task, they inevitably come up with different plans on different tries due to inherent nature of LLMs. Most companies like this step to be predictable, and they end up removing it from the system and doing it manually. Thus turning it into a workflow automation vs an agentic system. I think this is what people actually mean when they want to deploy agents in production. LLMs are great at automation*, not great at problem solving. Examples I have seen - customer support (you want predictability), lead mining, marketing copy generation, code flows and architecture, product specs generation, etc.
The next leap for AI systems is going to be whether they can solve challenging problems at companies - being the experts vs the doing the task they are assigned. They should really be called agents, not the current ones.