As AI agents begin making network changes, ‘test in production’ becomes a serious liability. A network digital twin lets teams verify every change before it goes live.
Ask a software developer where they test code, and they’ll describe a staging environment, version control, and an automated regression suite. Ask a network engineer the same question, and the honest answer, more often than not, is “in production.” This was standard practice when I was running networks more than 20 years ago, and it’s still the case. For decades, the industry has accepted making a change, watching what happens, and rolling back if something breaks. That was tolerable when teams made changes one at a time during a maintenance window.
That era is ending. As AI agents begin proposing and executing network changes, “test in production” shifts from a bad habit to a serious liability. That’s the core argument of a new e-book from Forward, The Network Digital Twin Guide, which contends that a mathematically accurate model of the network is a prerequisite for autonomous operations. Forward obviously has a stake in that conclusion, but the problem it describes is real, and I hear about it constantly from network leaders.
What a network digital twin is
The term “digital twin” is often used loosely, so it’s worth being precise. Forward defines a network digital twin as a software-based replica of the production network that captures every device, configuration, and path, and it can be queried to determine exactly how the network will behave.
The e-book draws an important distinction between two approaches that share the label. One emulates the network by running the actual device firmware against specific test scenarios. The other builds a deterministic mathematical model from the network’s configuration and state, computing all possible forwarding behaviors at once. The guide sums up the difference well: “An emulated replica tells you what happened when you tested it. A mathematical model tells you what will happen, for every path, every time.”
A digital twin also isn’t observability. Monitoring tools tell you what’s happening at specific points right now. A digital twin answers a different question: Given everything configured across every vendor, cloud, and layer, where can traffic actually go, and does that align with the business’s intent? Observability tells you the patient’s vital signs. The twin is closer to a full-body scan.
The problem with legacy network operations
Every network starts with a design that reflects the architects’ intended connectivity, security, and resilience. The moment it goes live, reality begins to drift from that design. Devices are added, rules are modified, exceptions pile up, and documentation falls behind. Forward calls this the “intent-reality gap,” and anyone who has inherited a firewall rule base with thousands of entries knows it well.
The bigger problem is how that uncertainty affects behavior. When no one can predict the blast radius of a change, teams stop making necessary changes. OS upgrades that patch known vulnerabilities are deferred. Firewall updates sit in review for weeks. The network becomes something the business works around rather than something that moves it forward.
The data the report compiles shows the cost. Citing Verizon’s 2025 Data Breach Investigations Report, the e-book notes that exploitation of vulnerabilities in network edge devices, such as VPNs, firewalls, and routers, grew nearly eightfold year over year, rising from 3% to 22% of breaches. The same report found that only 54% of network vulnerabilities are remediated each year, with an average remediation time of 32 days. That’s what deferred patching looks like at scale.
The operational and financial impacts are equally clear. The e-book cites Gartner’s estimate that an unplanned production network outage costs more than $500,000 per hour, and IBM and Ponemon Institute data showing that U.S. data breaches cost more than $10 million per incident, more than double the global average. On the delivery side, a routine firewall rule change can take weeks when every modification is reviewed manually against guesses about network behavior.
Why this must change in the AI era
AI is putting pressure on the network from two directions. The first is networking for AI. According to Gartner data cited in the report, more than half of data center switch spending will support AI workloads by 2028, up from less than 30% today. These networks are less tolerant of errors than traditional enterprise networks.
The second, and more disruptive, is AI for networking. Every vendor I speak with is building agents that can diagnose problems and, increasingly, take action. The e-book makes a point I strongly agree with: “An agent making an unverified change doesn’t fail differently than a human making the same mistake; it fails at machine speed, and potentially across many changes running in parallel.”
Change advisory boards can’t review hundreds of agent-proposed changes per hour. But removing humans without replacing their judgment with something more reliable is reckless. Large language models are inherently probabilistic, so the network needs a deterministic layer beneath them to check their work.
The value the digital twin brings
This is where the digital twin earns its place. Its most important capability is pre-change verification: running a proposed change against a production-equivalent model to see exactly how it will affect the network before anything touches production. That turns every change from a judgment call into a pass-or-fail test, whether the change comes from a senior engineer or an AI agent.
The benefits span teams. According to the e-book, NetOps can validate BGP, OSPF, and ACL updates before change-board review, reducing review time from extended peer checks to minutes of model execution. SecOps can test firewall rules for unintended access across the entire network. CloudOps can validate paths across public clouds and on-premises environments before workloads go live, and compliance receives continuous validation with a full audit trail.
The AI angle is the most compelling. The report argues that a twin’s AI assistant should run its analysis against the deterministic model and return the configurations, paths, and policies behind each answer. This gives Tier 1 and Tier 2 staff access to Tier 3 expertise and exposes verified answers to third-party agents via APIs and a Model Context Protocol server. The twin becomes the source of truth that other AI systems consult before acting.
Forward outlines a four-stage maturity path: behavioral truth, democratized insight, predictive pre-change verification, and, finally, safe autonomous execution. I like this framing because it honestly acknowledges that autonomy is the final step, not the first.
Recommendations for network professionals
- Get the foundation right before pursuing autonomy. Agents are only as good as the data they rely on. Build an accurate model of your network’s behavior before letting AI act on it.
- Measure your drift. Quantifying how far the network has deviated from its design, especially around segmentation and compliance boundaries, is a quick win that often reveals surprises.
- Make pre-change verification a required gate. Integrate the twin with ITSM, automation, and CI/CD pipelines so that no change, whether human or agent, reaches production unverified.
- Hold vendors to a high standard. Ask how many platforms and OS versions they support, and what evidence backs their accuracy claims. A twin that confidently answers questions about an incomplete model is worse than no twin.
- Bring security and compliance in early. Exposure analysis and continuous compliance are often where the fastest ROI is achieved.
- Insist on deterministic AI. Any AI assistant that connects to the network should show its work and base its answers on a verified model, not on inference.
Final thoughts
Networking is the last major IT discipline without a real pre-production environment, and the AI era is exposing that gap. The organizations that succeed with autonomous networking won’t be the ones that deploy agents first. They’ll be the ones that build the verification layer that enables them to trust those agents. The digital twin is that layer, and network professionals who start building it now will be in control when the agents arrive.
