# Comparison of Multi-Agent Platform Usability for Industrial-Grade Applications
## 5 AOSE in Contrast to Modern SDLC
### Automated Deployment and Monitoring Tools  
Due to the impact of agile methodologies, as outlined in Section 4, frequent deployments of small application features have become a standard in modern SDLC. As a result, automation of deployment processes has been set as the minimum requirement. This \begin{table}
\begin{tabular}{l l} \hline \hline
**Platform Name** & **Provided Test Support** \\ \hline \multirow{4}{*}{_JADE_} & Provides dedicated GUI for scenario testing. Does not offer any other means of \\  & test automation. Moreover, restricts access to the agents, limiting the possibilities of integration tests. \\ \multirow{2}{*}{_Jadex_} & Provides dedicated GUI for scenario testing. Does not offer any other means of \\  & test automation. \\ \multirow{2}{*}{_SPADE_} & Does not provide any dedicated means of test automation. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Test support in JADE, Jadex, and SPADE. includes, among other things, the flexibility of configuration propagation, which not only needs to encompass several technologies (as mentioned in Section 5.2) but also must accommodate differences occurring in the targeted environments. Equally important is the control over the application state after deployment, which can be streamlined using several monitoring tools (e.g., Prometheus) and dedicated dashboards (e.g., Grafana). Because of the architectural design and the complexity of distributed MAS, these aspects are even more challenging to address. None of the existing agent platforms fully support (1) automated methods for propagating deployment configurations and (2) integration with monitoring tools. In this domain, some potential has emerged with the creation of cloud-based agent platforms (e.g., CloneMap [69]). These could, for instance, leverage the automated possibilities of Kubernetes and compatible technologies. However, as of now, they have not been used to address the challenge directly. Accordingly, at present, deployments in AOSE must be performed manually (or, in the best-case scenario, semi-manually). This also applies to the monitoring tools, which are even more complex due to the necessity of verifying and tracking distributed systems. Such a solution does not meet the standards of production systems. In particular, in industrial-grade applications, this would be unacceptable due to the time and effort required from the developers. A deeper understanding of the severity of these issues can be seen by considering how they are addressed in JADE, Jadex, and SPADE, as summarised in Table 7.  
As can be observed, none of the platforms provide support for distributed automated deployment. This includes both configurations that need to be propagated to run distributed systems and scripts for allocating agents and running them in the company's environment using single deployment pipelines. Regarding JADE, these limitations and disadvantages have been encountered in a practical use case. As part of the scalability tests for the JADE agent platform, the JADE-based system was run on multiple Azure virtual machines located in different world regions [76]. Testing was performed using a variety of internal agent system architectures, where agents were distributed among many agent platforms and containers. In this setting, the pre-configuration of the test environment took up to four hours of the developers' time. Similarly, each reconfiguration of internal architecture required manual changes in initial scripts, taking around half an hour for each run. Moreover, it should be considered that these tests were conducted on a relatively simple infrastructure, which was at TRL 3-4. This emphasises even more that AOSE would not be advantageous in industrial-grade use cases (1) considering the investment necessary for deployment and (2) in contrast to the remaining SDLC technologies, which already provide a high level of automation. A lack of automation and built-in integrability can also be observed in terms of advanced monitoring.