From NetDevOps to AgenticOps: Real-World Use Cases with the Network MCP Docker Suite
5 min read
How AI assistants, powered by the Network MCP Docker Suite, transform troubleshooting, documentation, and compliance – and how this points towards Agent-First Operations.
This article is Part 3 of a 3-part series:
- Part 1: When Networks Understand What You Want – The MCP Protocol Revolution
- Part 2: Network MCP Docker Suite – Seven MCP Servers for AI-Driven Network Operations
- Part 3 (you are here): From NetDevOps to AgenticOps – Real-World Use Cases with the Network MCP Docker Suite
In Part 1, I explained the Model Context Protocol (MCP). In Part 2, I introduced the Network MCP Docker Suite and walked through a live troubleshooting demo. In this final part, I focus on real-world use cases, practical scenarios, and the shift towards AgenticOps.
Key Use Cases Transforming Network Operations
1. Unified Network Operations
Today, engineers can manage Meraki cloud networks, on-premises infrastructure, Catalyst Center deployments, and direct IOS-XE devices through a single conversational interface. As a result, constant context switching between dashboards and CLI tools becomes unnecessary.
Example prompt: “Show me all switches in Building A with uptime less than 30 days.”
2. AI-Powered Network Management
Natural language queries now automatically gather, correlate, and analyze data from multiple network management systems.
Example prompt: “Generate a report of devices that don’t match between NetBox documentation and Catalyst Center inventory.”
3. Automated Troubleshooting
Instead of running commands manually, I can let AI assistants investigate network issues by querying multiple systems, correlating data, and suggesting remediation steps.
Example prompt: “Why is our main website experiencing high latency from Europe?” (Uses ThousandEyes + Catalyst Center.)
4. Security & Compliance Operations
It is also possible to monitor security compliance across the network infrastructure with unified access to ISE, Catalyst Center, and configuration management.
Example prompt: “Show me all non-compliant devices and their authorization profiles.”
5. Infrastructure Documentation Automation
Keeping network documentation synchronized automatically is another powerful use case. AI can continuously detect discrepancies between DCIM (NetBox) and the actual network state.
Example prompt: “Update NetBox with all devices discovered in Catalyst Center over the past week.”
Real-World Success Stories
Scenario 1: Network Troubleshooting
Challenge: A NOC engineer needs to investigate why a critical access point is showing as unreachable in Catalyst Center.
Solution: Using an AI assistant connected to both Catalyst Center and IOS XE MCP servers, the engineer simply asks what’s wrong. The AI automatically:
- Checks device status in Catalyst Center
- SSHs into the device via IOS XE MCP
- Verifies physical connectivity via CDP
- Identifies an IP address mismatch as the root cause
- Suggests correction steps
Result: The issue is resolved in under 2 minutes instead of 20+.
Scenario 2: Infrastructure Documentation Sync
Challenge: A system administrator needs to ensure NetBox documentation matches the actual network state after several device deployments.
Solution: I simply query the AI: “Compare devices in Catalyst Center with NetBox and highlight discrepancies.”
The AI uses both the Catalyst Center MCP and NetBox MCP servers to automatically identify missing devices, outdated IP addresses, and incorrect site assignments.
Result: Documentation is updated in minutes with zero manual cross-referencing.
Scenario 3: Compliance Reporting
Challenge: An IT manager needs a quarterly compliance report showing device inventory, security posture, and policy compliance.
Solution: A single AI query is enough: “Generate compliance report showing all devices, their compliance status, and any ISE authorization issues.”
The AI correlates data from Catalyst Center (compliance), ISE (authorization), and NetBox (inventory) to generate a comprehensive report.
Result: The report is generated in minutes instead of days of manual data collection.
From NetDevOps to AgenticOps: The Evolution Continues
The Challenge: Traditional Ops is Outpaced
Today’s network operations teams face significant challenges:
- Massive technology disruption: cloud, AI, mobility, and internet convergence
- Skill shortage: many organizations face a shortage of experienced network and automation engineers
- Proliferation of agents: enterprises will run large numbers of AI agents across domains
- Speed requirements: traditional operations are outpaced, and AI is often the only way to keep up
Because of these forces, moving beyond traditional NetOps into NetDevOps and, eventually, AgenticOps is not just interesting – it becomes necessary.
Cisco AI Canvas and Deep Network Model
While the Network MCP Docker Suite provides open-source MCP servers for network operations, Cisco is also investing heavily in AI platforms such as
AI Canvas (intro video) and the Deep Network Model. These are designed to bring AgenticOps concepts into production environments at scale.
- Deep Network Model: a purpose-built networking model that aims to answer networking questions more accurately than general-purpose LLMs
- Trusted training: built on decades of Cisco networking expertise and learning content
- Cross-domain operations: designed to span campus, branch, data center, security, and observability domains
My Network MCP Docker Suite is not an official Cisco product; it is an open-source project I created to make these concepts tangible and usable in labs and real environments today.
How the Network MCP Docker Suite Fits In
For me, the Network MCP Docker Suite provides:
- ✅ An open-source foundation for building AI-ready network operations
- ✅ A concrete MCP protocol implementation that works with multiple AI assistants
- ✅ Multi-system integration (Cisco platforms, NetBox, ThousandEyes, Splunk)
- ✅ A learning platform for understanding AgenticOps concepts end to end
- ✅ Production-ready containers that can run in lab or real environments
The Future of Network Operations
The Network MCP Docker Suite represents a shift in how we interact with network infrastructure. As AI assistants become more capable, the gap between human intent and technical implementation shrinks dramatically.
Imagine a future where:
- Network issues are detected, diagnosed, and often resolved autonomously
- Documentation updates itself automatically as the network evolves
- Security compliance is continuously verified across all platforms
- Capacity planning uses AI to predict needs before they become critical
- Multi-vendor network ecosystems work together seamlessly
- Junior engineers operate with senior-level expertise through AI augmentation
- Mean time to resolution (MTTR) drops from hours to minutes
With MCP and practical projects like the Network MCP Docker Suite, I believe this future is much closer than many expect.
Get Started Today
If you want to explore these ideas hands-on, you can start with the open-source code:
Network MCP Docker Suite on GitHub
The repository includes everything you need to deploy the MCP servers, connect them to AI assistants, and try out the use cases I’ve described in this series.
Key Takeaways
1. MCP is a Protocol, Not an AI
Model Context Protocol (MCP) is an open standard that enables consistent interaction between AI agents and external data and tools – comparable to TCP for data. It is not an AI itself; it is the communication layer that makes AI-powered operations possible.
2. AI is Already Reshaping Network Operations
We are moving from traditional NetOps → NetDevOps → NetAIOps / AgenticOps. Manual operations alone are not enough anymore, and AI is becoming a required part of the toolkit.
3. The Network MCP Docker Suite Makes AIOps Accessible
With seven production-ready MCP servers, containerized deployment, and documentation, you can start implementing AI-driven network operations today.
4. Multi-System Correlation is the Killer Feature
The real power comes from correlating data across multiple systems – Catalyst Center, IOS XE, NetBox, ThousandEyes, ISE, Splunk, and more – and letting the AI reason over the combined context.
5. Open Standards Enable Innovation
By building on MCP, the suite works with any MCP-compatible AI assistant (Cursor, Claude Desktop, LibreChat, and future tools), which keeps the approach flexible and future-proof.
If you missed any part of the series, you can go back here:
- Part 1: When Networks Understand What You Want – The MCP Protocol Revolution
- Part 2: Network MCP Docker Suite – Seven MCP Servers for AI-Driven Network Operations
Note: The Network MCP Docker Suite is a personal open-source project created for demonstration purposes. It is not an official Cisco product. The examples and code are provided “as is” to illustrate how AI agents can interact with network APIs through the Model Context Protocol (MCP).
Authors
