A new decentralized Q-learning algorithm, IC-Q, has been developed for multi-agent workflows, allowing agents to coordinate without accessing joint trajectories. This advancement could enhance the efficiency of AI systems in complex organizational settings by providing finite-sample guarantees under decentralized partial observability.