> For the complete documentation index, see [llms.txt](https://dashpay.gitbook.io/roboflux-whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dashpay.gitbook.io/roboflux-whitepaper/3.-modular-architecture.md). # 3. Modular Architecture The RoboFlux AI platform is architected around a microservices-based modular framework, enabling independent scaling, deployment, and upgradeability of functional components. Each module communicates via lightweight, encrypted asynchronous message brokers, ensuring minimal latency and optimal throughput in distributed environments. * **Module-Oriented Abstraction:** Distinct containers host the anomaly detection, pathfinding, webhook management, and report generation subsystems. * **Dynamic Service Discovery:** Utilizes decentralized service registries and health-checked load balancers for operational resilience. * **Failover & Redundancy Protocols:** Auto-scaling clusters and redundant instance replicas maintain system availability under load surges and partial failures. By decoupling system functionalities into independent services, RoboFlux AI ensures ease of extensibility, continuous integration/deployment (CI/CD) pipelines, and isolated fault domains. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://dashpay.gitbook.io/roboflux-whitepaper/3.-modular-architecture.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.