Agentic State Machines
goose launched a long time ago. Dog years have nothing on Agent years. goose predates general agentic AI by a bit. The initial thought behind it all was: what happens if you give an LLM control over a shell session?
Turns out interesting things happen. The model writes a command, the command runs, the result goes back into the conversation and the model decides what to do next. Our tool calling grew naturally out of that experiment, as did the agent loop that now sits at the heart of most coding agents.
Then tool calling got formalized and MCP happened. So did approvals, context management, todo lists, top-of-mind, subagents, retries, hooks, skills, recipes and steering. But the original agent loop remained. Each feature added another branch, another local variable or another special case. A dozen different concerns might be active inside one turn, each influencing the others. Cancellation needs to know whether inference or a tool is running. Compaction needs to understand provider errors and usage. Tools can change the prompt. A stop hook can turn a completed answer back into more work. The loop started to turn into spaghetti.
