Presented by Cisco
AI agents are breaking traditional IT operations models, adding complexity, data silos, and fragmented workflows. DJ Sampath, Cisco’s SVP of AI Software and Platform, believes that AgenticOps is the solution: a new operational paradigm where humans and AI collaborate in real time to create efficiency, boost security, and allow for innovative technological applications.
In a recent conversation with VentureBeat, Sampath outlined why current enterprise IT management is fundamentally breaking and what makes AgenticOps not just useful, but necessary for IT operations going forward.
The breaking point of traditional IT operations
The core problem plaguing enterprise IT today is fragmentation, Sampath said.
“A lot of times inside of these enterprises, data is sitting across multiple different silos,” he explained. “For an operator to come in and start troubleshooting something, they have to go through many different dashboards, many different products, and that results in an increasing amount of time spent trying to figure out what is where before they can actually get to the root cause of an issue.”
This challenge is about to intensify dramatically. As AI agents become ubiquitous within enterprises, the complexity will multiply exponentially.
“Every single person is going to have at least 10 or more agents that are working on their behalf doing different types of things,” Sampath said. “This problem is only going to be tenfold, if not a hundredfold worse when you start to think about what’s really happening with the inclusion of agents.”
Three core principles of AgenticOps
To address these challenges, Cisco has developed its AgenticOps capabilities around three fundamental design principles that Sampath believes must be true for this new operational model to succeed.
First, unified data access across silos. The platform must bring together disparate data sources: network data, security data, application data, and infrastructure data.
“Bringing all of that stuff together is going to be incredibly important so that the agents that you are deploying to do work on your behalf can seamlessly connect the dots across the board,” Sampath said.
Second, multiplayer-first design. AgenticOps must be fundamentally collaborative from the ground up, enabling IT operations, security operations, network operations teams — and agents — to work together seamlessly.
“When you bring the IT ops person, the SecOps person, the NetOps person all together, you can troubleshoot and debug issues a whole lot faster than if you’re working in silos and copy pasting things back and forth,” he explained. “It’s humans and agents working together in a synchronous environment.”
Third, purpose-built AI models. While general-purpose AI models excel at broad tasks, specialized operations require models trained for specific domains.
“When you start to go into specializations, it becomes really important for these models to understand very specific things like network configuration or thread models that you care about and needs to be able to reason about that,” he said.
How Cisco operationalizes AgenticOps across the enterprise stack
Cisco’s approach unites telemetry, intelligence, and collaboration into a single coherent platform. Cisco AI Canvas is an operations workspace that replaces multiple dashboards with a generative UI and a unified collaborative experience. Within AI Canvas, operators can use natural language to delegate actions to agents — pulling telemetry, correlating signals, testing hypotheses, and executing changes — while maintaining human-in-the-loop control.
The reasoning capabilities come from Cisco’s Deep Network Model, trained on over 40 years of operational data including CCIE expertise, production telemetry, Cisco’s Technical Assistance Center (TAC), and Customer Experience (CX) insights. This purpose-built model delivers domain-specific intelligence that general-purpose models cannot match.
Cisco’s platform spans campus, branch, cloud, and edge environments, allowing agents to consume telemetry across the entire ecosystem at machine speed, including Meraki, ThousandEyes, and Splunk. With MCP servers implemented across Cisco products, agents gain standardized access to tools and data without custom integration work.
How fragmented reporting data undermines IT troubleshooting
The traditional approach to IT troubleshooting involves raising tickets and piecing together fragmenting information across multiple systems.
“People take screenshots. Sometimes it’s in Post-it notes,” Sampath said. “All of this information stays in completely different channels so it becomes really hard for somebody to start collecting them together.”
Cisco AI Canvas addresses this by giving teams one shared, real-time workspace for the work at hand — so context doesn’t get scattered across chats, tickets and screen shares. Teams can collaborate live, escalate instantly, and contribute context (such as screenshots and notes) alongside the agent’s generated charts and graphs. But the real power emerges when AI agents join these collaborative sessions.
“The machines are constantly learning from these human to machine interactions,” Sampath explained. “When you see that same problem happen again, you are that much faster in responding because the machines can assist you.”
This creates a virtuous cycle of continuous improvement, where the agent asks if you’d like to continue using the same approach as last time, for example, and you’re able to hand over more work to the agent. And the time spent debugging gets compressed as the system learns and accelerates future responses.
Security as an AI accelerator
Historically security has been considered a roadblock to adoption and even innovation. But with the right guardrails, organizations can confidently deploy AI at scale, and even accelerate it.
Employees have already experienced the productivity gains of tools like ChatGPT and want similar capabilities within their enterprise environments. When organizations can detect personally identifiable information, prevent prompt injection attacks, and maintain proper data governance, they can unlock and unleash the AI adoption inside of the enterprise in a fundamentally different fashion.
The identity layer required for cross-domain AgenticOps
Cross-domain data access presents one of the most complex challenges in AgenticOps implementation. Cisco’s strategic acquisitions, particularly Splunk, position the company to address this, unifying data across traditionally disconnected systems. But bringing data together is only half the battle, since who has access to what data becomes vitally important.
Cisco is evolving its Duo platform beyond multi-factor authentication to serve as a comprehensive identity provider, with robust identity and access management baked into the platform from the beginning, not bolted on as an afterthought.
“We’re investing in identity as a very core pillar of how these agents are going to be able to pull data from different data sources with the right authorization in mind,” explains Sampath. “Should this agent have access to this type of data? Should you be correlating these types of data together to be able to solve a problem?”
Humans in the loop, but at a higher level
As AI agents become more autonomous, the role of humans will evolve rather than disappear.
“We’re always going to have humans in the loop,” Sampath said. “What you’re going to see is the complexity of the tasks that are being performed are going to be a lot more involved.”
Take coding as an example, which today can be entirely agentic. The human role has shifted from manual coding, or even tab completion, to asking an agent to create code wholesale, and then verifying that it meets requirements before merging it into the codebase. This pattern will repeat across IT operations, with humans focusing on higher-level decision-making while agents handle execution. Importantly, rollback capabilities ensure that even autonomous actions can be reversed if needed.
Why waiting for AI to ‘settle down’ is the wrong move
For CIOs and CTOs, the message is clear: don’t wait.
“A lot of folks are in this holding pattern of waiting and watching,” Sampath said. “They’re waiting for AI to settle down before they make some of their decisions. And I think that is the wrong way to think about this. A partnership with the right groups of people, with the right sets of vendors, is going to help you go a whole lot faster, as opposed to trying to just stay on the fence, trying to figure out what’s right and what’s wrong.”
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