Artificial intelligence is moving past the era of simple chatbots and static models. The next phase, loosely called agentic operations, or agentic ops, promises systems that don't just answer questions but act autonomously within defined boundaries. That was the subject of a recent conversation between host AB and Akshay Bhargava, Cisco's VP of Product Management for AI Software and Platform.

Bhargava, whose role places him at the intersection of AI product strategy and platform engineering, laid out a vision where AI agents become active participants in enterprise workflows rather than passive tools that require human prompting for every step. The term "agentic" has been gaining traction across the industry, but Cisco is one of the first major infrastructure companies to publicly frame its product roadmap around it.

What is agentic ops?

Agentic ops refers to a class of AI systems that can perceive an environment, make decisions, and execute actions with minimal human intervention. Unlike traditional automation, which follows rigid if-then rules, agentic systems use large language models and reinforcement learning to adapt to novel situations. They monitor system health, detect anomalies, correlate events across telemetry streams, and even trigger remediation workflows without waiting for a human operator to open a ticket.

The concept is not entirely new — cybersecurity has long used automated response playbooks. But the difference today is the breadth of context an agent can hold. A modern AI agent can ingest logs, understand network topology, check change management systems, and reason about the impact of a configuration change before executing it. That level of contextual awareness was previously impossible outside of heavily curated rule sets.

Cisco's position in the AI stack

Cisco is best known as a networking and security hardware vendor, but over the past few years it has been repositioning itself as a software and platform company. Bhargava's group is responsible for the AI layer that runs across Cisco's portfolio, from network controllers to security analytics. The company has invested heavily in telemetry and observability, which are the raw materials for any agentic system. Without good data, an AI agent is just guessing.

What Cisco brings to the table is the ability to embed agentic capabilities directly into the infrastructure that enterprises already run. Instead of bolting an AI layer on top of existing tools, Cisco is trying to make the network itself intelligent. That means agents that can reroute traffic based on application performance, or automatically quarantine a device that exhibits suspicious behavior.

Bhargava's conversation with AB explored how this evolution changes the role of IT operations teams. Rather than writing runbooks and monitoring dashboards, operators will define objectives and constraints. The agent handles the execution. The operator steps in only when the agent's confidence drops below a threshold or when a decision has regulatory implications.

The evolution of artificial intelligence

The broader theme of the discussion was the evolution of artificial intelligence itself. Bhargava traced the arc from predictive models that forecast system failures, to generative models that produce natural language summaries, to agentic models that take action. Each stage builds on the last, but the agentic step is the one that most directly affects how work gets done.

Predictive AI reduced uncertainty. Generative AI reduced the time to produce reports and documentation. Agentic AI reduces the need for human intervention in routine operations. For enterprises running large-scale IT environments, that last reduction has a direct impact on staffing costs, incident response times, and system reliability.

Bhargava did not offer a concrete timeline for when agentic ops would become mainstream, but the direction is clear. Cisco is already shipping products with agentic features, and the company's platform strategy is designed to make those features additive rather than disruptive. Customers do not need to rip and replace their existing tools to try an agentic approach. They can start with a single use case — auto-remediation for a specific type of network fault, for example — and expand from there.

Limitations and open questions

No conversation about agentic AI would be complete without acknowledging the risks. Autonomous systems that touch production infrastructure must be trustworthy. A hallucination in a generative model is annoying; a hallucination in an agentic model can take down a data center. Bhargava highlighted the importance of guardrails, auditing, and human-in-the-loop validation as essential components of any agentic deployment.

There is also the question of scope. An agent that handles a single, well-defined task is easier to build than one that manages end-to-end incident response across multiple domains. Cisco's approach appears to favor incremental adoption: small, bounded agents that prove their reliability before being given broader authority.

Privacy and compliance are another layer of complexity. Agentic systems must respect data locality, access controls, and retention policies. Cisco, with its deep roots in enterprise networking, is likely designing its agentic platform to inherit the security posture of the underlying infrastructure. But the details of how that works in practice remain to be seen.

What comes next

For IT leaders watching the AI space, agentic ops represents a genuine shift in what is possible. The technology is not science fiction. It is shipping today, albeit in limited forms. Companies like Cisco are betting that the next competitive advantage in enterprise IT will come not from better hardware but from smarter, more autonomous software.

Bhargava's conversation with AB offered a glimpse into that future without overselling it. The tone was measured. The examples were concrete. The message was: this is coming, and you should start preparing your data and your teams now. The transition from dashboards to agents will take years, but the first steps are already being taken.

SysCall News will continue to track how agentic ops develops across the industry. Cisco is one of several major vendors moving in this direction, and the competitive landscape will shape how quickly the technology matures. For now, the key takeaway is that AI is no longer just a tool for analysis. It is becoming a tool for action.