Cisco Network Platform Executive: AI Agents Executing Network Changes Must Be Deterministic, Not Improvisational
Joe Vaccaro, Senior Vice President and General Manager of Cisco's Network Platform and ThousandEyes, explained Cisco's four-stage AI network operations framework in a podcast, highlighting a core design constraint: networks require deterministic outcomes rather than probabilistic ones. Therefore, during the execution phase, predefined workflows must be used, and AI agents are not permitted to improvise changes.
Network operations are facing a scale problem: the volume of alerts has exceeded human processing capacity. Joe Vaccaro, Senior Vice President and General Manager at Cisco's Network Platform and ThousandEyes, admitted on the Targeting AI podcast that if humans alone were responsible for resolving these alerts, companies simply could not hire enough people. This pressure is driving enterprises to increasingly trust AI agents to execute network changes directly, bypassing the need for step-by-step manual approval.
To address this, Cisco designed a four-stage framework: sensing issues, diagnosing them using cross-domain telemetry, planning remediation, and executing changes. While AI is utilized across all four stages, the execution stage faces a hard constraint—networks require deterministic results, not probabilistic ones. Vaccaro explained that when reaching the execution stage, where changes are actually applied across the network, deterministic execution is essential. Cisco's approach is to use predefined workflows at this stage rather than allowing AI agents to improvise. This means that while AI can decide what needs to be done, how it is done must follow a predefined, fixed process.