Forward Networks, which is rebranding to simply Forward, has launched Forward Predict, a capability that validates proposed network configuration changes against a digital twin of the production environment before those changes are deployed. The twin spans physical devices from multiple vendors as well as major cloud environments including AWS, Azure, Google Cloud, and IBM Cloud.
When a proposed change fails verification, the platform returns specific failure data rather than a generic error, which Forward says allows AI agents to iterate on the configuration until a passing result is produced. The product is currently in beta and will be generally available in fall 2026.
Network operations has historically lacked a pre-production test environment. Software development teams have relied on staging environments for decades to catch errors before they reach production, but network infrastructure has not had a direct equivalent. Change management in enterprise networking typically involves weeks of planning, formal method-of-procedure documentation, and Change Advisory Board review processes.
Despite that overhead, configuration changes still regularly produce outages, because the production network remains the only environment where a change can truly be observed. Forward Predict is positioned to close that gap.
The underlying product is an extension of Forward's existing digital twin technology, which models network device state from the network layer through the application layer and maps all possible packet paths and policy conflicts.
Predict extends that model to future states, computing how the network would behave after a proposed change is applied and returning what Forward describes as deterministic results.
The distinction between a probabilistic risk assessment and a deterministic verification matters commercially. Deterministic outputs are what would be required for an autonomous system to act on verification results without human review.
Forward is not positioning Predict as a standalone change management tool though. The framing is that verified, deterministic outputs are a prerequisite for agentic AI systems to propose, validate, and execute network changes at machine speed.
Forward is betting that its digital twin becomes the verification layer that AI agents depend on, which would make it infrastructure for a broader autonomous networking stack rather than a point solution.
That positioning invites comparison to how observability and monitoring platforms have been reframed as essential substrates for AI-driven operations.
The customer list, which includes Goldman Sachs, PayPal, and S&P Global, suggests Forward has traction in regulated, change-sensitive environments where the cost of a misconfiguration is high.
Those are also environments where autonomous execution will face the most scrutiny and the longest path to full deployment. The near-term case for Predict is likely the reduction of human review overhead in change management rather than full autonomy.
The autonomous networking framing sets a longer-term product trajectory, but the immediate value is in compressing change cycles and reducing outage risk in organizations that currently treat every network change as a high-stakes event.
As AI agents take on more operational tasks, the question of how those agents confirm their work before acting becomes a structural problem.
Forward is making the case that pre-deployment verification against a complete network model is how that confirmation works. Whether that position holds will depend on how the agentic networking market develops and whether Forward's twin can maintain fidelity as networks grow in scale and heterogeneity.
