For years, the network has occupied an unusual position in enterprise technology.
There is no doubt that the network is critical. Every application depends on it, every cloud service traverses it, and every security control ultimately relies on it. Yet compared to cloud infrastructure, software development, data platforms, and more recently AI, networking has for some time been treated as a mature operational discipline rather than a strategic innovation domain.
Cisco’s Cloud Control announcement at Cisco Live 2026 suggests that may be changing.
Cloud Control is not simply another management platform. It represents Cisco’s vision for how enterprises will operate technology in the agentic era. Human operators and AI agents work together inside a shared operational environment, using common context and common data to manage networking, security, observability, collaboration, and AI infrastructure.
As Cisco President and Chief Product Officer Jeetu Patel described it:
“AI agents reason and act continuously at software speed, and that changes everything about how we scale, manage, and defend our critical infrastructure.”
The most interesting aspect of the announcement may not be Cloud Control itself. It may be Agent Builder.
Cisco announced that Agent Builder in Cloud Control Studio will support integration with more than 50 third-party platforms and tools through native connectors and Model Context Protocol (MCP). On the surface, that sounds like a straightforward ecosystem announcement. But there’s more to it, because it shows how Cisco views the market, which is in essence, that no platform is an island when it comes to agentic operations.
The future will consist of AI agents reasoning across infrastructure, security, observability, collaboration, cloud, and networking domains simultaneously. The value will come not from isolated agents, but from coordinated systems capable of acting across organizational and technology boundaries.
That shift has important implications for networking.
As Bob Laliberte of TheCube Research observed in coverage of the launch:Â
“Networking is cool again. As we move to these highly distributed environments, the network becomes a key enabler for operationalizing AI.”
That observation captures something many infrastructure teams have already begun to recognize. AI may be the headline, but the network is the operational foundation that determines, practically speaking, whether agentic systems succeed or fail.
Why Networks Are Different
Most infrastructure domains have spent years moving toward disposability. Servers can be rebuilt. Containers can be recreated. Cloud resources can be reprovisioned automatically. Modern infrastructure increasingly assumes failure and designs around it.
Networks are different.
As Omdia analyst Jim Frey observed during Cisco Live coverage:
“Compute systems are cattle — servers and containers and virtual machines are all designed to be disposable. But networks are still like pets.”
A networking mistake rarely remains isolated. A single configuration error can affect applications, users, security controls, cloud connectivity, and business operations simultaneously.
That creates a unique challenge for AgenticOps. AI agents can reason over large datasets. They can identify patterns. They can generate recommendations. They can even determine likely corrective actions. But when those actions affect production networks, raw intelligence alone is not enough.
The challenge when dealing with a domain where ripple effects can be so broad, is governed execution: ensuring that a proposed action can be validated, authorized, and safely executed on the specific devices, operating systems, software versions, and configurations that exist in the real world.
The Missing Layer in Agentic Operations
This is where many discussions about agentic infrastructure become disconnected from operational reality, particularly when dealing with the messy realities of mainstream enterprise networks.
Most enterprise networks are not greenfield environments. They are brownfield, multi-vendor, multi-generational environments that have evolved over years or decades. They contain different operating systems, different management models, and different configuration approaches. Many contain thousands of devices spread across data centers, campuses, branches, cloud environments, and remote locations.
Adding AI on top of network complexity does not eliminate that complexity. In many cases, it increases the consequences of misunderstanding it.
Practically speaking, successful Agentic NetOps will require a foundation of trusted network automation. After all, organizations cannot expect AI agents to safely operate infrastructure that humans have not yet learned to automate, validate, and govern consistently themselves.
AI agents need access to systems capable of understanding what actually exists, validating proposed actions against operational reality, and governing how changes are made. They need an execution layer capable of bridging the gap between what an AI agent wants to accomplish and what can safely be performed in production. Â
Remember, safety is not a trivial matter when agents have so much more ego than wisdom.
For networking, that execution layer may ultimately prove just as important as the intelligence layer itself. That is the problem Gluware was built to solve.
Where Gluware Fits
Cisco’s announcement included Gluware within the third-party platform and tool ecosystem for Agent Builder.
An integration would allow AI agents operating within Cloud Control to interact with Gluware’s Intelligent MCP Server powered by DIAL when network actions are required.
DIAL, Gluware’s Device Interaction and Automation Layer, was built to solve the execution challenge. Developed over more than one million engineering hours across nearly 305 releases and validated against environments containing up to 450,000 network nodes, DIAL discovers infrastructure across vendors, operating systems, and generations of equipment. It extracts, abstracts, and translates network configurations into a continuously validated intent model that can be used to automate operations safely at scale.
Every action is validated through DIAL-powered pre-checks and post-checks before and after execution. Every change can be verified, governed, and audited. Every workflow operates against a foundation that understands the actual state of the network rather than assumptions about what the network should look like.
The same foundation powers capabilities across the Gluware Titan AI platform. Titan Exposure Management, for example, uses DIAL’s feature mapping intelligence to identify precisely which devices in a multi-vendor, multi-operating-system environment are actually affected by a given CVE. Rather than relying solely on software version matching, Titan evaluates the specific features and capabilities present on each device, dramatically reducing false positives and helping organizations focus on actual risk.
That distinction becomes increasingly important as organizations move from AI-assisted operations toward AI-executed operations.
Cisco is building a vision for how humans and AI agents will collaborate to operate enterprise infrastructure, but realizing that vision will require specialized capabilities across multiple domains. The Cloud Control ecosystem recognizes that reality.
For networking, the future will not be defined solely by better AI reasoning. It will be defined by governed execution.
Preparing for the Agentic Future
The significance of Cloud Control extends beyond the platform itself. It signals where the industry is heading.
Organizations are moving toward a future where AI agents help operate infrastructure, investigate problems, execute workflows, and accelerate decision-making. That future will require trusted operational foundations capable of translating intent into action safely, consistently, and at scale.Â
The future of network operations is agentic. The organizations that succeed will be the ones that first build a foundation capable of governing execution.
If your network is not fully automated today, that doesn’t mean the agentic future is out of reach. DIAL automatically discovers and models existing infrastructure across vendors, operating systems, and generations of equipment, helping organizations move from manual operations toward governed automation without years of custom development.
If you’re ready to prepare your network for agentic operations, request a demo and we’ll show you how Gluware can help transform brownfield complexity into an automation-ready foundation in days and weeks rather than months and years.
