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  Why Your AI Agent Keeps Falling Over — And How State Machines Fix It State management and orchestration in the age of agentic AI Picture an agent three steps into a five-step task. It has already called an API, parsed a response, and written a partial file to disk. Then step four throws an exception — a rate limit, a malformed JSON blob, a tool that just doesn't respond. What happens next? If your agent is built as a linear chain — prompt in, tool call, prompt in, tool call, repeat — the honest answer is: nothing good. The chain doesn't know it failed halfway. It doesn't know what "halfway" even means. It just breaks, and someone has to clean up the mess by hand. That fragility isn't a bug in any one implementation. It's a structural property of chains themselves, and it's the reason serious agent frameworks have quietly moved on to something else: graphs and state machines. The Chain Was Never Built for This Early LLM pipelines borrowed their shape ...