## 1 Introduction
Moreover, it is a competing approach for microservice-based design with proactive and reactive intelligence. MANA-NMS used a MAS due to its advantage over microservices in terms of autonomy and intelligence. In the proposed ANF, the agents are autonomous with adaptive functionality in performing a given task. An ANMS can be built composing these service agents. Alternatively, the MANA-NMS framework can be taken as a splitting of a monolithic system into agents and recomposing them to obtain an autonomous system: e.g., SDN controller evolved packet core (EPC), and network management system (NMS). Currently, network function such as EPC, SDN, and NMS are monolithic systems that can be split into the smallest possible functions [14; 15]. The decomposed units can be implemented as microservices [14] or agents [12]. This is an appealing paradigm in the current trend of softwarization, service function virtualization or containerization, cloudification, and orchestration. This helps deploy network functions (possibly autonomous agents) as loosely coupled, elegant, and lightweight applications in a virtualized/containerized, distributed, and cloud/edge environment with maximum re-composition freedom. Both refs. [12; 13] only discusseda theoretical model, and the performance evaluation is performed using mathematical evaluation. A similar approach with a focus on an agent-based algorithm was proposed in [16]. The authors presented a software agent to execute a set of computation loads in parallel, which enables a bottom-up systematic organization of the overall system. The paper discussed IoT objects to be an agent that is autonomous and able to collaborate with other peer agents. However, the approach is focused on high-level objects and does not consider protocol level and function level functionalities. Moreover, it is needed to consider service design approaches in comparison with microservices showing how to incorporate ML/DL algorithms in the agent design. In this paper, using the design guideline from [12], we implement the internal composition of an NTPA and NTCA. We briefly introduce the ML and DL algorithms that are used as the cognitive component of NTCA and NTPA. We also propose and implement possible communication interface technologies. In addition to the legacy agent communication language (ACL), we suggest Restful-API, gRPC, and Websocket. This paper could be considered a continuation toward the implementation of MANA-NMS, the automation architecture proposed in [12] and an extended version of the work in [17]. The authors in [17] only considered classification agent design with limited evaluation that does not consider the resource consumption of agents. This is crucial in that agents are mostly deployed in a containerized environment with a resource-constraint environment, especially as the complete system may need a number of agents to be instantiated. In [12], the authors used mathematical models and evaluation and left the implementation as future work. Therefore, this article tries to implement and simulate the ML/DL-based NTPA and NTCA and evaluate critical measures, such as accuracy and latency. The benefit and advantage of designing NTPA and NTCA agents are to use these agents as a service unit in the multi-agent system. In the simulation section, an example of the complete MANA-NMS system architectures is provided, incorporating the traffic classifier agent and traffic predictor as well as their role in the overall system as an example. The contributions of this work are as follows:  
* Design the internal architecture of NTPA and NTCA. * Implement and simulate both agents. * Evaluate the performance of NTPA and NTCA using accuracy prediction and classification, training latency, prediction and classification latency, and resource consumption, respectively. The rest of the paper is organized as follows. Section 2 presents an overview of the network automation and enabling technologies.