In this research, a new framework is studied and presented to design a guidance law for a multi-agent interception system, which is used to tackle high-speed targets. These targets are not easily recognizable, so accurate estimation of their position is not available at far distances, and measurement systems only detect a range of target presence. On the other hand, due to various uncertainties in the dynamical modeling of the interceptors, the designed guidance and control laws must be. To deal with the uncertainty of target identification according to the statistical properties of the measurement systems, we consider the target as an envelope. To tackle this envelope, we define a virtual hull on the interceptor side, which is used to guide the agents in the middle phase. In this phase, the points that are optimally selected are placed in the virtual hull, and each agent is directed to these points. To do this, first, the problem of extracting these optimal points is investigated, and by solving it, the optimal formation is extracted inside the hull. Then, the multi-agent guidance law is designed with the differential game approach using dynamic adaptive programming. The problem is constrained by collision avoidance, input saturation, and disturbance. Integral reinforcement learning is used for training. In this approach, each agent follows a path that results in multi-agent formation tracking. Next, due to the importance of increasing efficiency, optimal design, and cost-effectiveness, an integrated guidance and control law is designed. In this method, considering the disturbance constraints, parametric uncertainty, and input saturation, an integrated guidance and control law is proposed using extended state observer and command filter. Unlike the mid-phase, the final phase is functionally single-agent. In the end, an integrated simulation for high-speed target tracking is performed, in which the mid-phase and final phase are implemented using the proposed integrated guidance and control law. It is shown that the proposed framework improves target interception. Key Words : Robust Guidance, Multi-Agent System, Adaptive Dynamic Programming, Disturbance and Input Saturation, Integrated Guidance and Control