Selector AI Launches Foundry to Bring Deterministic Agentic AI to Network Automation

Selector AI has announced the launch of Selector Foundry, an agentic AI platform engineered to correlate telemetry across multi-vendor networks, cloud infrastructure, and application stacks to autonomously diagnose root causes and execute deterministic remediations. The platform ingests telemetry from network hardware, software management systems, and Model Context Protocol (MCP) data sources into a unified data model. Specialized AI agents then evaluate contextual anomalies, isolate root causes, propose targeted actions, and interface with operational systems including ServiceNow, Jira, PagerDuty, Slack, and Microsoft Teams. For network engineers and NetOps leaders, the significance lies in bridging the long-standing gap between passive telemetry monitoring and active remediation. Enterprise networks supporting distributed cloud and AI training clusters cannot afford extended troubleshooting cycles or configuration drift. Point automation scripts frequently fail when encountering unexpected cross-domain state changes. By introducing specialized agent suites that apply deterministic reasoning to multi-vendor telemetry, organizations can execute reliable, closed-loop corrections without exposing critical routing and switching topologies to the hallucinations common in generic generative models. This release reflects a broader paradigm shift across cloud and infrastructure automation: the transition from static Infrastructure as Code (IaC) and fragmented AIOps dashboards toward autonomous, agentic workflows. While early AIOps solutions focused primarily on alert suppression and threshold anomalies, the modern NetDevOps mandate requires active operational integration. The integration of standard interface frameworks like MCP highlights how infrastructure tooling is converging around open agent protocols, allowing automation agents to safely query network state, orchestrate fixes, and update IT service management records within governed boundaries. In practice, engineering teams should evaluate Agentic NetOps platforms with a focus on determinism, auditability, and guardrails. Autonomous agents should initially be deployed in advisory modes—where they correlate signals and generate proposed change tickets—before being granted direct write or configuration execution privileges. NetOps practitioners must ensure that underlying telemetry pipelines provide high-fidelity, synchronized state data, as agent reasoning is only as robust as the data model it evaluates. Establishing explicit change validation policies and rollback hooks will be vital before moving automated agent remediation into production core and edge networks.