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Forward introduces a solution to a fundamental, long-standing gap in networking: the lack of a deep, end-to-end behavioral understanding of complex, multi-vendor environments and the inability to test intended configuration updates safely. To address these vulnerabilities, Forward developed a mathematically accurate digital twin platform that connects to all physical, virtual, and cloud-based network devices. By collecting device states and configurations, the platform proactively traces every potential packet path to create a reliable behavioral model. Building upon a decade of monitoring current and historical network behaviors, Forward recently launched Forward Predict, a foundational technology that allows operators to safely simulate, analyze, and refine multi-vendor network modifications in a production-equivalent sandbox before actual deployment.
The presentation features a live, operator-driven demo where a complex cross-site service migration, routinely requiring several weeks for enterprise network teams to plan and execute, is successfully simulated and validated by an engineer in just 15 minutes. Using an integrated, syntax-aware AI command window, the engineer designs the migration from a legacy data center to a new spine-and-leaf facility. The platform’s deterministic analysis quickly reveals that while the primary connection goals are achieved, a critical security regression is introduced that inadvertently exposes the payroll server to the internet. Catching this risk at design time allows the operator to instantly append a zone-based firewall rule to the multi-vendor change set, rerun the predictive analysis, and ensure all predefined compliance and security checks pass flawlessly before generating a deterministic verification report for final change control approval.
Beyond manual operator workflows, Forward Predict accelerates business agility by translating its full functionality into REST APIs that seamlessly integrate into existing change management systems. In a standard ServiceNow workflow, Predict automatically reviews proposed changes during the assessment phase to document risk levels, propagation ranges, and regressions before final approval. For organizations utilizing automated CI/CD pipelines, the technology can be embedded directly into Jenkins and Ansible playbooks to serve as an automated pre-approval circuit breaker, halting the pipeline if any network anomalies or security vulnerabilities are detected. Ultimately, this predictable validation loop serves as a blueprint for truly autonomous networking, empowering emerging AI networking agents with the logical reasoning and deterministic tools necessary to iteratively propose, test, and safely execute network changes at scale without the risk of breaking production environments.
Personnel: Mike Lossmann, Nikhil Handigol
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