Forward AI Demo Troubleshooting Network Operations with Forward Networks

Nikhil Handigol’s presentation showcases how Forward AI revolutionizes network operations troubleshooting. Before diving into the AI capabilities, Handigol provided a concise tour of the foundational Forward Enterprise platform. This robust, deterministic software connects to all network devices across hybrid multi-cloud and multi-vendor environments to create detailed, point-in-time snapshots of network configuration and behavior. The platform offers various analytical views, including graphical topology, inventory dashboards, vulnerability assessments, blast radius analysis, and precise path tracing. A key component is the Network Query Engine (NQE), which transforms raw configuration data into a normalized, hierarchical data model and supports queries via a SQL-like language, enabling users to extract specific network insights and verify compliance against predefined checks, triggering alerts when discrepancies arise.

The core demonstration focused on how Forward AI, as a conversational interface, streamlines resolving common network connectivity issues. By ingesting a service ticket describing a host’s inability to reach a database server over SSH, the AI agent dynamically constructs and executes a diagnostic plan. This plan involves gathering context about the involved hosts and performing a precise path trace through the network’s digital twin. In the scenario presented, Forward AI swiftly identified the issue: SSH traffic was blocked by a specific firewall due to an explicit Access Control List (ACL) deny rule. Crucially, the system provides a clear, “bottom line up front” diagnosis, supported by detailed explanations of the blocking device, the rule, and the full traffic path, all substantiated with direct links to the relevant “evidence” views within the Forward application, enhancing transparency and user trust.

Extending its utility, Forward AI can also generate proposed Command Line Interface (CLI) commands as a starting point for resolving identified issues, such as creating a new firewall security policy. Nikhil strongly emphasized that these generated fixes are for planning purposes only and require human validation and adherence to established operational change procedures, underscoring that the system does not autonomously execute changes. Discussions highlighted essential guardrails, including the AI’s ability to reject unanswerable requests and the enforcement of Role-Based Access Control (RBAC) to restrict data access and command generation based on user permissions. While a feedback mechanism (thumbs up/down) is in place to gather user input for continuous improvement, future iterations may incorporate business policies into AI recommendations and develop simulation capabilities within the digital twin before deploying changes to production, further building trust and enhancing automation.