The objective of this research is to provide appropriate cross-layer architecture for wireless cognitive networks for efficient resource allocation and improved quality of service. We proposed the blackboard model, which is known for coordinating multiple agents (cognitive nodes) in a real-time manner, receiving the current state of information from these nodes, providing conclusions basing on information received from these nodes, updating these nodes with current conclusions, and suggesting needed actions for these nodes. Each cognitive node is assumed as an agent to the blackboard. The parameter values abstracted from these cognitive nodes to blackboard are structured messages that are optimized with respective to an objective function. This paper introduces the cross-layer design of a cognitive network, the role of the blackboard architecture, and possible applications.