AI agents can now propose network changes at machine speed, yet your team still has no staging environment to prove those changes before the 2 AM change window.
Forward Predict, now generally available, closes that gap. Built on Forward Enterprise’s mathematically accurate digital twin of your entire production network, it tests proposed changes at design time and reveals their impact on connectivity, security, and compliance before a command ever hits production. Forward Predict brings software engineering discipline to NetOps, providing for the first time network equivalents of staging environments and unit testing. Whether a change originates from an engineer or an AI agent, Forward Predict provides the auditable proof needed to iterate quickly and execute safely.
Safe Agentic NetOps depends on knowing what a proposed change will do before AI acts. While AI agents can propose and execute changes at machine speed, they cannot act safely on incomplete context, stale data, or assumptions about how your network will behave. Forward Predict provides the proof layer between AI intent and production. Changes that an agent proposes are tested end-to-end against the actual state of your network before execution. When a proposal fails validation, specific evidence explains why, giving your agents and your engineers the information needed to revise and resubmit until the change is verified. Forward Predict tracks every iteration as a versioned change set, so you can audit every step.
Forward Predict provides the proof layer between AI intent and production. Changes an agent proposes are tested end-to-end against the actual state of your network before execution.
Nikhil Handigol, Forward’s Chief AI Officer and co-founder, explains: “‘Know before you act’ is more than a tagline; it’s the operating model Agentic NetOps requires. AI agents can propose change at machine speed, but speed without proof of correctness just accelerates risk. Forward Predict verifies the impact of proposed change using Forward’s mathematically accurate digital twin before execution. That gives agents the evidence they need to operate safely and gives engineers the confidence to move faster.”
Knowing before AI acts lets your organization move from human-in-the-loop to human-on-the-loop operations as a controlled progression, with proof at every step. It is a critical advancement as the industry moves from safer Agentic NetOps toward trusted autonomous networking.
Since the beta launched in May, organizations in financial services, media, and technology have used Forward Predict to reduce change risk and accelerate delivery. IG Group is one of the beta customers. Steve Bamford, Senior Network Engineer at IG Group, said: “We’ve been using Forward Predict for a few months now and continue to find new use cases. We extended it from validating our BGP and routing changes to our firewall rule workflow, where automated path checks now catch requests for rules that aren’t even necessary before they’re implemented. That’s cut our firewall change delivery time by roughly 95 percent and is saving our engineers more than 30 hours of manual work every month.”
“[Forward Predict] cut our firewall change delivery time by roughly 95 percent and is saving our engineers more than 30 hours of manual work every month.”
Network changes made by your AI agents or engineers carry three main risks: security exposure, connectivity failure, and compliance violations. Forward Predict harnesses a mathematically accurate digital twin of your entire hybrid network, modeled at full scale across multi-vendor on-premises infrastructure and major public clouds, including AWS, Azure, Google Cloud, and IBM Cloud. Before deployment, it checks each change for:
Your application team needs traffic to pass between two network segments, requiring a few new ACL lines on a core switch. Peer review can judge only the intent of those lines. Forward Predict models the edited ACL on the device and runs reachability and isolation checks across the full digital twin, against your network as it is today and as it would be after the change. You can confirm that the requested flow will work and that the isolation it depends on will remain intact. If the edit opens an unintended path, you see it before the change window, not during it.
Less uncertainty means less operational overhead for your teams. Your design iterations against the digital twin return verified results in minutes. Reviews that once took days of sequential manual checking can close within hours. Your Change Advisory Board (CAB) approvals can rest on verified evidence instead of judgment calls. Your networking, security, cloud, and compliance teams work from one verification layer, and every result is auditable.
Forward Predict is available today. Request a demo to watch it verify an AI-proposed change end-to-end, or visit the Forward Predict page for details.
