This paper presents the Never Ending Open Learning Adaptive Framework (NEOLAF), an integrated neural-symbolic cognitive architecture that models and constructs intelligent agents. The NEOLAF framework is a superior approach to constructing intelligent agents than both the pure connectionist and pure symbolic approaches due to its explainability, incremental learning, efficiency, collaborative and distributed learning, human-in-the-loop enablement, and selfimprovement. The paper further presents a compelling experiment where a NEOLAF agent, built as a problem-solving agent, is fed with complex math problems from the open-source MATH dataset. This paper presents the Never Ending Open Learning Adaptive Framework (NEO-LAF), which is an integrated neural-symbolic cognitive architecture. It can be used to model and construct intelligent agents, such as self-improving intelligent tutor agents in an adaptive instructional system environment.