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AI systems fail differently from traditional software. A rogue agent makes an innocent-looking database query that chokes the DB, congests the network, and triggers a cascade of downstream failures — none of which appear in the agent's own logs. Meanwhile, your telemetry bill keeps climbing while your team still can't find root cause fast enough. FFWD is the AI infrastructure intelligence platform any AI app — Claude, Copilot, Codex, or your own autonomous infrastructure agent — can consume through MCP. We don't ship another agent for you to adopt. We give the AI app you already use the AI infrastructure depth no one else delivers: silicon-to-semantic telemetry, cross-domain anomaly correlation, and execution risk scoring before agents act on production. Four interlocking capabilities on one stack: High-performance telemetry pipeline — ingest, filter, enrich, and route millions of events per second, multi TB per day. FFWD is built in Rust. No vendor lock-in. Full-stack anomaly detection — our Signal Correlation Model applies eight distinct ML model categories across logs, metrics, traces, GPU telemetry, and container events. No rules to write. No topology map required. Operational from day one. Cross-domain correlation — when anomalies appear in unrelated systems at the same time, FFWD's marker correlation engine finds the statistical link. Root cause hidden in a GPU thermal trend, a sequence anomaly in a Python service, and a network rate spike? FFWD connects them. Agent Assurance — a pre-task execution risk score before infrastructure agents act, and a confidence score after. A quantitative Go/No-Go gate grounded in live full-stack anomaly intelligence, not static rules. These aren't four products. The pipeline feeds the anomaly model. The anomaly model powers the risk score. The risk score is only trustworthy because the data underneath it is complete. Built for engineering teams running AI agents on production infrastructure.