By Alex Henthorn-Iwane, Senior Vice President of Marketing, Gluware
Titan AI is Gluware’s agentic capability for network automation, helping engineers automate more accurately and extending that skill beyond a small circle of experts. It runs on Gluware’s Proof of Agentic Trust framework, pairing agentic reasoning with independent, deterministic validation so outcomes are accurate, not just plausible. We spoke with a network leader at a large enterprise that operates in a regulated industry, about how his team uses it.
How is your team using Titan AI today?
Titan lets someone verify something before they do it. I’ve turned it over to our Level 2 engineers, for example, if they’re unsure how to set a configuration on a Cisco device, I have them ask Titan first. I use it heavily for config modeling: how to build a proper model, or help with syntax so I don’t pull the wrong thing when I execute it. I’m also using it to grow my own Gluware skills, so I open fewer support tickets.
You mentioned syntax and config modeling specifically. Why does getting that exactly right matter so much in your environment?
In networking, “probably right” isn’t an acceptable standard to operate on. If someone in our team asks an AI a question at the command line and it gives back wrong information on a core router, that could turn into a major incident. I work for an organization where our infrastructure supports sensitive and regulated operations, so I have to be extra cautious. I still run validated Gluware config models on network test lab devices before production because these systems are that critical. An automation product in this type of setting has to work out of the box. There’s zero tolerance for error.
Given that bar, what gives you confidence in what Titan actually produces?
It’s not one agent trusting another agent’s answer. Titan validates what it generates using the device translation layer built into the Gluware platform. My experience is that it works. I’ve put in a regex I thought was correct and asked it to check my work, and it’s caught mistakes, like one wrong backslash that would have meant something completely different. There’s also a simulator that runs the logic against Gluware’s tool call validations without applying it, so I see the outcome before it touches the network. I think of Titan as our double-checker.
Beyond catching mistakes, how has that changed who on your team can build automation?
There’s less fear of trying to automate. Raw scripting or playbooks or Terraform takes syntax fluency most of us don’t use often enough to build into muscle memory. Titan gives less experienced engineers a phone-a-friend, so they can ask a question and get guided to the answer instead of being blocked. That lowers the friction to get into automation.
Where do you see that heading for your team longer-term?
Once people aren’t afraid of the syntax, they skill up: how do I design this well, not just run it. It’s the shift the industry went through with lower-level coding — nobody writes machine code anymore, because it became abstracted. That made engineers more productive, not less capable. Agentic assist means we can move away from having to focus on the lower-level aspects of automation, toward being able to architect for the outcomes the business wants. A good agentic layer pushes the whole team up that ladder.
Standing back, what’s the overall value to your organization?
Work that took several weeks in raw scripting mode now takes well under a week in Gluware, largely because Titan removes the guesswork. But the bigger value is that it lets more of my team build automation confidently, in an environment where we simply cannot afford to be wrong.
