the real test of an autonomous agent isnt how many actions it can complete. its what happens when one fails. @TheARCTERMINAL says it ran five autonomous agents on Ethereum across 27 action types, with onchain settlement and no central controller. the 27 actions are interesting. the failure path is more interesting. does the agent detect the failure? does it retry safely? can it stop before one bad step cascades? can another agent continue without inheriting the error? does the record make the failure obvious afterward? autonomy looks impressive when everything works. reliability shows up when it doesn't. thats why i'd rather see agent benchmarks include recovery quality, not only successful execution. AI agents are moving from chat interfaces into systems that change external state. at that point, graceful failure becomes a capability. if you were evaluating an autonomous agent, would you care more about success rate or recovery behavior?