Bring AI Infrastructure intelligence to GPT, Claude, Copilot - or any agent you already use.
FFWD delivers cross-domain telemetry, anomaly correlation, and agent assurance through MCP. No new dashboard. No new agent. No platform migration.
From today's human driven AI Apps, to full autonomous agents of the future.
Infrastructure agents fail in ways nobody is watching for.
AI agents acting on production infrastructure don't fail with errors. They improvise — route around constraints, retry with workarounds, find alternate paths. From the inside it looks like normal operation: no errors, reasonable latency. From the outside, an infrastructure agent pushes a config change that isolates a data centre. It scales the wrong service and burns through cloud budget in an hour. It restarts a database during peak load. It mis-provisions network capacity and breaches SLAs across thousands of customers at once.
Today's AI safety watches what agents say. Almost nobody watches what agents do to the infrastructure they're acting on — or whether that infrastructure was stable enough to act on safely. The clues aren't inside the agent. They're scattered across the operations stack the agent lives in.
FFWD answers one question for every infrastructure agent: is it safe to let this agent act right now?
When the answer is no, FFWD blocks the next action before it executes.
Three layers. One MCP interface. Any AI app or agent.
Pipeline collects. Intelligence correlates. Assurance gates. All three exposed to whichever AI app your team already uses — Claude, Copilot, GPT, or your own infrastructure agent.
Assurance Layer — Nio Agent Guard
The Go/No-Go gate at the action moment.
Nio sits in the agent's action path. An Execution Risk Score combines agent behaviour, full-stack infrastructure health, and the blast radius of the proposed action — into one quantitative verdict, delivered to your AI app through API before every critical action.
When the score crosses your policy threshold, Nio gates the next action: blocked, confirmed by a human, or allowed. After execution, the Intelligence Layer correlates impact and feeds it back to the score. Pre-action restraint and post-action learning, in one closed loop.
Intelligence Layer
Anomalies, correlation, root cause — across everything.
Eight ML model families run across silicon, network, application, and agent behaviour simultaneously, then correlate across them to surface causal chains.
Findings arrive as a natural-language root-cause advisory — readable by your engineers through Claude, or consumed directly by your infrastructure agents through MCP. The same correlations feed the Assurance Layer's risk score above. No rule libraries to maintain. No alert tuning to keep current.
Data + Pipeline Layer
The foundation everything sits on. Silicon-to-semantic.
A Rust-native pipeline scaling linearly to millions of events per second across 50+ connectors — GPU metrics, Kubernetes, SNMP, Kafka, OpenTelemetry, Syslog, and more. Forwards in parallel to whichever destinations you already use: Datadog, Elastic, Kafka, S3, Splunk, OpenSearch, Azure Blob, GCS.
Deploys where your data lives.
Private. On-premise. Air-gapped. Your telemetry never leaves your environment.
FFWD runs entirely inside your perimeter — on-prem, private cloud, or air-gapped. No telemetry leaves your environment. No external SaaS dependency. Built for enterprises where data sovereignty and regulatory compliance are the starting point, not an option.
“We, SAKURA internet, Inc., is constantly pursuing innovations to help our customer’s business run more efficiently and smoothly.
We are very honoured to contribute their success by offering FFWD, a superior observatory services on our IaaS service, “SAKURA’s Cloud” , (so-called, Logging as a Service) provided in SaaS format through this partnership.”
“Capabilities such as infrastructure self-recovery and fast time to restoration after failure are very important to our business. Logs and events are essential clues and triggers to achieving these capabilities. FFWD helps us to affordably collect, analyse and monitor our infrastructure logs and events in real-time, improving our operational reliability and efficiencies.”