Systems of Systems (SoSs) integrate many critical systems our society relies on. In designing individual systems, stakeholders use bespoke protocols, custom information models, and proprietary components with limited computational resources from various vendors. We present a reference architecture that allows multiple stakeholders to carry out a flexible integration without giving up control to a single entity in the presence of the aforementioned limitations. Our architecture relies on rule engines and graph data model to integrated systems flexibly even when black-box components are used. At the same time, a federation of the rule engines allows each stakeholder to retain control over the rules that reflect their policies. We also rely on a common information model based on ontologies to account for the information model mismatch and reduce the duplication of integration efforts. Moving rule execution to the standalone rule engines allows deployment in resource-constrained and proprietary environments. A uniform application programming interface (API) is used to integrate rule engines across systems as well as components within each system with a respective rule engine. We also present a novel algorithm to determine dependencies across rules deployed in different rule engines within the federation. This allows domain experts to develop rules as usual without having to deal with the distributed aspect of the system. We also present a proof of the sufficient condition to ensure all necessary notifications will be sent to ensure correct rule activation across different rule engines. Compared to other systems involving distributed rules, the proposed architecture is well-suited for the integration of transactional workloads commonly found in enterprises. The qualitative evaluation based on the Architecture Tradeoff Analysis Method (ATAM), applied to a telecommunications use case, shows that the architecture possesses the “interoperability”, “modifiability”, and the “functional completeness” quality attributes with a trade-off around rule expressiveness. The quantitative evaluation demonstrates speedup over the single-node setup in most scenarios except in case of highly optimized rules and a poor network performance simultaneously (tr<10ms, tn=100ms).