Network change has long been one of the most anxiety-inducing responsibilities in IT operations — a high-stakes process where a single miscalculation can trigger a 2 a.m. outage and weeks of post-mortem review. Forward, formerly known as Forward Networks, is looking to eliminate that fear entirely with the launch of Forward Predict, a technology that deterministically computes how any proposed change will behave across the full production network before a single command is ever deployed. Built on the company’s existing network digital twin platform, Forward Predict validates routing changes, including BGP, OSPF, and static routes, alongside firewall, ACL, NAT, and segmentation changes across both on-premises infrastructure and major cloud environments including AWS, Azure, GCP, and IBM Cloud.
What separates Forward Predict from conventional change management approaches is its mathematical model of the entire production network, spanning every vendor and every layer from L2 through L7 in a single unified representation. The technology arrives just months after the January 2026 introduction of Forward AI, which brought conversational and agentic capabilities to the Forward Enterprise platform. Together, the two technologies form a closed-loop system where AI agents can propose, test, and refine network changes inside a safe, production-equivalent environment, iterating until a verified outcome is confirmed, without ever touching live infrastructure. VMblog sat down with Nikhil Handigol, Chief AI Officer and Co-Founder of Forward, to get the full story.
VMblog: Let’s kick things off by asking, what is Forward Predict?
Nikhil Handigol: Forward Predict enables network and security teams to predict how a change will impact the production network with mathematical accuracy. Built on the Forward Enterprise network digital twin, it proves the outcome of any proposed change before anything touches production, eliminating the risk that has made network change one of the most feared tasks in the industry.
Forward Predict validates routing changes including BGP, OSPF, and static routes, alongside firewall, ACL, NAT, and segmentation changes, all verified against a mathematically accurate production-equivalent model before deployment. Security, connectivity, and compliance risks are identified and eliminated at design time, across cloud environments including AWS, Azure, GCP, and IBM Cloud as well as on-premises networks.
The business impact is immediate. Change reviews that previously took months compress to minutes, with verified evidence behind every approval. New applications, cloud expansions, and AI workloads stop being held up by change processes. And for teams moving toward autonomous networking, Forward Predict verifies AI-proposed changes before they execute, giving agents a safe environment to iterate toward verified outcomes before anything touches production.
VMblog: What makes Forward Predict fundamentally different from existing solutions in the market? How does it build upon what you announced earlier this year with Forward AI?
Handigol: In January 2026, we introduced Forward AI, bringing conversational interaction and agentic capabilities directly into Forward Enterprise so that engineers of any experience level can perform sophisticated network analysis using natural language. Forward AI is native to the platform and connects to the broader ecosystem of enterprise systems and agents, forming an essential part of any agentic workflow.
Forward Predict introduces an entirely new category of technology. Built on Forward Enterprise, it deterministically computes the future behavior of the network by applying proposed changes to a mathematical model of the full production network. It models both the control plane and the data plane, capturing distributed protocols such as BGP and OSPF and accounting for how they interact and converge. Every vendor, every layer from L2 through L7, on-premises and cloud, all unified in a single model. Not a sample, not a representative subset, not a single-vendor abstraction. The entire production network, reproduced with mathematical precision.
VMblog: AI agents making autonomous network changes sounds powerful, but also risky. How does Forward Predict prevent an AI agent from iterating itself into an even worse state?
Handigol: An AI agent working with Forward Predict never gets to iterate on the live network, which means there is no risk to production. Every action it proposes is validated against the predictive digital twin first. The end-to-end impact of a proposed change is analyzed within the digital twin, and the AI agent discovers any mistakes or unintended consequences and resolves them before committing a single command to production.
That validation step is what closes the loop. If a proposed change introduces an outage risk, a security exposure, a compliance violation, or any other unintended consequence, the agent receives specific feedback from the digital twin about what failed and why. It then adjusts and resubmits alternative proposals until it lands on a change that is fully verified – all at design time, all before anything touches production. Forward Predict is the only technology that makes this closed-loop iterative path possible. This capability will enable AI to have the same transformative effect on networking that it has already had on software development, where coding agents iterate in staging environments, not in production.
VMblog: For a network engineer who’s been burned by a change window gone wrong, what does their day-to-day look like after deploying Forward Predict?
Handigol: If you have ever lived through a change window that went sideways, you know the worst part of the job is not the work itself. It is the unknown. Every significant change carries the real possibility that something undetected will surface at 2 a.m. The institutional response has been to add more process, longer review boards, more approvers, more post-change conference calls trying to reconstruct what went wrong. Forward Predict is built to remove the cause of that anxiety and recapture that time.
When a change is verified against a mathematically accurate model of the production network before it is committed, your day changes in concrete ways. Changes succeed on the first attempt and produce the intended outcome, which means rollbacks and retries stop being a routine part of the workflow. The cycle from design to delivery compresses from weeks or months to hours, and risk is eliminated.
The meaningful shift is where the reclaimed engineering hours go. Time previously consumed by manual verification, change-board preparation, and post-mortem diagnosis is returned to the engineer, and it tends to flow toward the work engineers actually want to do. Business initiatives that depend on the network, including new services, cloud expansions, and AI workloads, stop being slowed by change processes.
The cultural change is harder to capture in a bullet point, but it is the one engineers tend to mention first. The network stops being the reason the business is waiting.
VMblog: How does Forward Predict make autonomous networking more accessible?
Handigol: Autonomous networking has been talked about for years, but the obstacle has never been the intelligence of the agent. It has been trust. AI agents need deterministic and comprehensive behavioral data to produce the right reasoning, and they need a safe place to test their reasoning before it touches the production network. Without both, autonomy stays theoretical, because no operator is going to hand control of critical infrastructure to a system whose conclusions cannot be verified.
Forward Predict supplies both halves of that foundation. The digital twin provides deterministic behavioral data that exactly mirrors the complete production environment, which gives agents ground truth to reason against rather than approximations or vendor-specific views. Forward Predict then provides the safe testing environment, a mathematically accurate representation of the entire production network where every AI-proposed change is validated before it executes. Security, connectivity, and compliance risks are surfaced in advance. When a proposed change doesn’t produce the correct outcome or introduces an unintended consequence, the agent receives specific failure feedback and iterates until it arrives at a change that is fully verified. Every decision the agent makes and every outcome it produces is recorded as evidence, available for human review whenever an operator wants to inspect the reasoning behind a change.
That combination is what makes autonomous networking practical rather than aspirational. It gives AI agents the same conditions that allowed AI to transform software development. Deterministic data to reason with, a deterministic environment to test in, an auditable trail of how each decision was reached, and a clear bar for what counts as correct before anything reaches production.
VMblog: Why are you changing your name from Forward Networks to Forward?
Handigol: It was always there. Forward was the first word of Forward Networks, and honestly our customers figured it out before we did. They had been calling us Forward for years, so we decided to make it official.
But the name change reflects something real. The network digital twin has always been the foundation, and that does not change. What changes is the ambition. We are moving toward a world where every organization that depends on its network can run it autonomously, intelligently, and without fear of change. Forward felt like the only word that captured that direction.
For customers, nothing changes in terms of the technology, the team, or the commitment. What changes is the scale of what we intend to make possible for them.
Less explanation, more momentum. That is what the name means to us.
