What is agentic operations (AgenticOps)?

From AI to agents: how we got here

Unlike traditional AI-powered IT operations (AIOps), which stops at alerts and recommendations, AgenticOps goes further with AI agents that reason through problems and act at machine speed. Humans stay in the loop, not in silos—working side by side with agents through one shared workspace instead of juggling multiple panes of glass. The result is a new kind of cross-domain operation: problems are prevented or resolved faster, tickets shrink, and IT adapts in real time. For years, AIOps promised smarter IT—but it largely meant better alerts and prettier dashboards. Useful, but still reactive. It stopped at detection and left resolution to human teams.

The progression:

• Rules and scripts (1980s–1990s): If/then automation for repetitive tasks.
• Machine learning (2000s–2010s): Algorithms for anomaly detection and pattern recognition.
• Generative AI (early 2020s): Models that produce fluent explanations, summaries, and answers, but still reactive and limited to single-turn responses.
• Agentic AI (mid-2020s): AI systems that operate as agents—autonomous entities capable of reasoning step by step, sequencing tasks, accessing tools and data, and coordinating with other agents or humans to achieve a goal.

What’s an AI agent?

AI agents are autonomous software entities that don’t just respond—they decide and act. Unlike chatbots or dashboards that only present information, agents understand human-assigned goals and context, plan and execute tasks, adapt to conditions, and continuously learn.

Reliable agents at enterprise scale are defined by five attributes:

• Identity and context: A clear role, purpose, and scope.
• Reasoning: They break down complex problems, weigh alternatives, and make context-aware choices.
• Scale: They operate continuously across always-on systems.
• Security: Actions are bounded by policy, permissions, and audit trails.
• Operational efficiency: By combining reasoning with automation, agents reduce manual effort and accelerate outcomes.

Together, these traits make agents more than assistants. They are collaborators—able to work with other agents and with humans to drive operations forward.

Agentic and orchestration layers

Agents don’t operate in isolation. Sometimes they hand tasks off directly to one another—for example, a monitoring agent passing data to a diagnostic agent, or a remediation agent logging outcomes to a learning agent. These are straightforward, well-scoped interactions.

But when workflows grow more complex, the agentic layer steps in. It manages collaboration across multiple agents: breaking down tasks, parallelizing work, reconciling conflicting results, and maintaining shared context. The agentic layer relies on models—foundation, fine-tuned, or domain-specific—to help agents divide work intelligently and coordinate their reasoning.

Above that sits the orchestration layer. This layer ensures actions happen in the right order and within clear policy guardrails—for example, gathering telemetry before diagnostics begin, or validating fixes before remediation. While orchestration is workflow-driven, it can also use models to interpret operator intent, validate steps, and produce reasoning traces.

Within the orchestration layer, the Model Context Protocol (MCP) provides the connective tissue. Unlike traditional APIs—where each integration is custom and limited—MCP standardizes how agents connect to models, tools, and data. It gives orchestration a consistent way to let agents safely discover, access, and use the resources they need. This makes interactions more flexible, scalable, and governed than point-to-point integrations alone.

Together, the agentic and orchestration layers provide the structure that makes autonomous action reliable, explainable, and repeatable.

Why AgenticOps now?

Modern IT is too complex, too fast, and too fragmented for humans alone. AIOps improved visibility, but it stopped at alerts and recommendations. This is why we need to move from AIOps to AgenticOps.

AgenticOps vs. AIOps

In short: AIOps helped IT see problems sooner. AgenticOps enables IT to solve them—at the speed and scale modern environments demand.

The Cisco approach

The Cisco approach to AgenticOps is to unite telemetry, intelligence, and collaboration into a single, coherent framework. The goal is to make agents reliable at enterprise scale—able to reason with context, act safely, and work seamlessly alongside human operators.

• Identity and context: In Cisco AI Canvas, each agent has a defined role—monitoring, diagnostics, remediation, learning—so workflows are traceable, auditable, and collaborative.
• Reasoning: The Deep Network Model is trained on 40+ years of Cisco operational data—CCIE expertise, production telemetry and Cisco insights—so agents can reason with accuracy and depth that general models can’t match.
• Scale: The Cisco platform spans campus, branch, cloud, and edge. Agents consume telemetry across the Cisco ecosystem, including Meraki, ThousandEyes, and Splunk, at machine speed. And with MCP servers stood up across Cisco products, agents gain a standardized, scalable way to access the tools and data they need.
• Security: Every action is governed by encrypted access, transparent architecture, and reasoning traces. Operators retain oversight with the ability to validate, approve or override at any point.
• Operational efficiency: Cisco AI Assistant and AI Canvas provide a natural-language workspace where humans and agents collaborate in real time, replacing multiple panes of glass with a unified experience.

Together, these capabilities with Cisco make AgenticOps multi-data by design, multi-player across NetOps and SecOps, and powered by a purpose-built model—all with autonomy you can trust.

In practice

Picture a branch office slowdown.

• A monitoring agent detects anomalies in ThousandEyes telemetry.
• A diagnostic agent correlates those findings with Meraki wireless logs and Splunk insights.
• For this more complex workflow, the agentic layer coordinates multiple agents—splitting tasks, running path traces in parallel, and reconciling results.
• Here, agents tap the Cisco Deep Network Model, applying domain-specific reasoning to recognize patterns and root causes that generic models might miss.
• The orchestration layer, using the MCP, sequences the next step under policy: validating data before remediation proceeds.
• The root cause emerges: a misconfigured router on a redundant path.
• A remediation agent proposes a validated fix. Through Cisco AI Assistant, the engineer reviews and approves, and the agent applies it automatically.
• A learning agent records the workflow, updating reasoning traces so the system improves next time.

All of this is surfaced in AI Canvas: telemetry, reasoning, actions, and validations in one place. The Cisco Deep Network Model provides the intelligence, agents do the heavy lifting, and humans stay in control.

The outcome: no war rooms, no guesswork, no delay—just resolution at machine speed, with trust and transparency built in.

Risk and responsibility

Agents can act in a split second—but trust is earned. That’s why with Cisco every action is explainable, transparent, and reversible. Today, you stay in control. Over time, as confidence builds, you can let AI take on more—knowing autonomy has been engineered to be trustworthy from the start.

Cisco is building this path deliberately:

• Transparent architecture, encrypted access, and audit trails ensure accountability.
• The Deep Network Model delivers domain-specific expert-level results.
• MCP servers standardize and secure how agents connect to tools and data.

Together, these make AgenticOps not unchecked automation, but a trustworthy framework for autonomy.

The road ahead

AgenticOps is more than faster fixes—it’s the operating system for the future of IT. Digital twins, drift detection, and continuous learning will push operations from reactive firefighting to proactive prevention. As trust builds, agents will take on more, moving from supervised steps to autonomous resolution—always explainable, transparent, and governed.

Cisco is already laying this foundation. By unifying telemetry, reasoning, and collaboration across networks, clouds, and security, we’re engineering autonomy that can be trusted at scale.

The shift is clear: AIOps helped you see problems. AgenticOps helps you solve them.