An AI agent looks like magic in a demo and like plumbing in production. Underneath the branding, it is a loop: the model observes the current state, plans a next step, calls a tool, reads the result, and repeats until the goal is met or it runs out of room.
Three things decide whether that loop holds up. First, the context window, since every step has to fit inside a finite budget and long tasks quickly overflow it. Second, tools, which are the only way an agent touches the real world, so sloppy tool calls and result handling break it fast. Third, error handling, because a demo runs on the happy path while a real task eventually hits a tool that fails or a plan that was wrong.
Memory ties it together. An agent only stays coherent over a long run if it can store what matters and feed back just the relevant pieces each step, instead of cramming everything into one prompt.
If your agent dazzles once and falls apart on the second real task, the fix is almost always in that plumbing, not a bigger model. Full breakdown here: https://www.autolearningagents.com/how-ai-agents-work/
