Introducing Aviz Network Copilot™: Autonomous Agentic AI for Modern Network Operations

TL;DR

Network Copilot, powered by the Aviz Network AI Agent platform, provides a seamless, language-based interaction experience with the entire infrastructure. Network Copilot integrates seamlessly with your existing networking, security, observability, and ITSM tools from different vendors, and no data is moved or rip-and-replace is needed. Network Copilot offers query-time correlation, custom AI agents, progressive automation and choices for deployment to get troubleshooting down to minutes, increase efficiency and operations through automation, and make life easier for network operations.

Introduction

The enterprise network today is awash with high volumes of telemetry, logs, configs, tickets, and operational data from multiple vendors and different management platforms. As much as enterprises may have invested heavily in network management tools, engineers still find themselves sifting through fragmented tools to perform tasks such as:

• Finding the root cause of incidents.
• Validating changes or deployments.
• Carrying out operational tasks such as finding the latest config.

Aviz Network Copilot (NCP) is an Autonomous Agentic AI for network operations that addresses this gap. Instead of another management platform or data store, Network Copilot leverages intelligent agents to interface with your controllers, Observability tools, security tools, ticketing system, knowledge base, to return control over network operations back to you. With a natural language interface, you and your team can derive insights, automate work flows and execute complex, multi-step troubleshooting exercises from a single pane of glass.

How does Aviz Network Copilot simplify multi-vendor network operations?

Almost no enterprise environment uses a single vendor for all of its networking. Most use a mixture of Cisco, Arista, Juniper, SONiC-based switches, Palo Alto Networks, Fortinet, and a myriad of operational tools. Network Copilot offers a common language that is a layer of abstraction over vendor-specific commands and work flows.

Instead of visiting separate dashboards, or remembering syntax specific to a certain platform, users can state in their natural language how they want something to get done, and Network Copilot translates it into a series of commands against the entire connected infrastructure, enabling organizations to create standards without compromising on the freedom of choosing their own vendors.

Network Copilot also supports a Bring Your Own LLM model, allowing organizations to select an AI model of their choice that matches their compliance, security, and operational needs.

What makes Network Copilot different from traditional AI assistants?

Many AIs necessitate an organization move all their operational data into a new repository first. This leads to expensive, long, implementation projects, endless data sync concerns, and multiple instances of hardware infrastructure. Network Copilot takes a different route with our query-time correlation architecture.

We avoid creating yet another silo of data and plug directly into the existing operational information – including network telemetry, logs, configurations, runbooks, policies, tickets and even other operational documentation.

Data is normalized at query-time as you ask questions; there’s no upfront data engineering projects. The system employs a neurosymbolic reasoning engine to blend language and symbolic reasoning in to execute multi-step troubleshooting, temporal event correlation, root cause analysis and intelligent retrieval planning; going beyond answering questions to sophisticated operational reasoning.

How does Agentic AI automate network operations?

But Network Copilot is not just for conversations, it helps your organization to transition into a truly autonomous world. Here, Autonomous agents will execute all your operational workflows. With our new Agent SDK and Sandbox, your organization will be able to design and develop your very own agents.

All your runbooks, escalation playbooks, and operational processes will be encoded in your agents.

These self-service agents will have access to the same underlying intelligence engine, Data Connectors and Integrations as the out-of-the-box functionality, thus providing a seamless development experience. You can start off with your first agents working alongside your network engineers to help them troubleshoot issues and collect contextual information and as you gain more confidence, gradually introduce Autonomous agents running in the background, identifying network health issues, correlating network events, detecting anomalies and take automated actions, requesting human intervention only for a final decision or a approval that needs human judgement. RBAC gives your admins a complete view of the users who can assign and run your agents as well as control their access to specific organizational data and network devices.

What operational use cases does Network Copilot support?

Network Copilot brings operational intelligence to network ops, empowering operations teams with NetOps automation, real-time performance metrics, exec-level reporting, and traffic analysis for capacity planning and anomaly detection. Network engineers can find devices by interface type, IP, hardware, transceivers, location and much more in minutes by simply querying the data with natural language. Security audits can also be done by correlating Access lists, firewall, ACLs, naming conventions and much more against policy baselines relevant to the organization.

Support teams can also check hardware lifetime, software exploits, end-of-support dates and vendor advisories for risk remediation. Network Copilot also assists with the change management process tracking firmware changes, configuration drift, hardware upgrades and compliance pre and post-maintenance windows. Integration with Zendesk and ServiceNow allows for faster incident resolution by correlating with incident tickets and providing a high-level executive overview of the root cause.

How does Network Copilot integrate with existing enterprise infrastructure?

A key advantage of Network Copilot is its leverage of investments the enterprise has already made. It integrates natively with controller solutions such as Aviz ONES, Cisco Nexus Dashboard and Cisco Catalyst Center, and is extensible to observability solutions including SolarWinds, IP Fabric, Forward Networks and NetBox. Organizations can use log ingest tools including Splunk, Loki, ELK Stack, InfluxDB, and retrieve data via Snowflake.

Network Copilot extends to chat platforms including Slack and RocketChat, and ITSM tools like ServiceNow and Zendesk.

Knowledge stores in formats including PDF, DOCX, Markdown, CSV, TXT, and XLSX are integrated into AI workflows, often with minimal upfront processing required. It also offers an Agent SDK, as well as MCP servers for those wishing to build unique automation, and allows teams to operate and extend across this infrastructure with the common reasoning engine.

Can Network Copilot meet enterprise security and deployment requirements?

Security and adaptability of your AI infrastructure are what matter when investing in the enterprise version of Network Copilot. Its adaptability offers many deployment options to suit various security and compliance requirements: Those bound by strict compliance measures can opt for an on-premises or air-gapped setup with your own private LLMs using their in-house GPU equipment. Companies prioritizing speed of deployment can deploy their own private clouds on AWS, Microsoft, or Google Cloud, retaining full control of their AI infrastructure.
For teams seeking an extremely speedy, off-the-shelf solution, the platform also comes in the form of GPU-as-a-Service and LLM-as-a-Service that requires no management of your own AI hardware.
Ultimately, organizations can make their own choices when it comes to infrastructure costs vs. speed of deployment and data control.

Conclusion

Data security and flexible deployments are key to enterprise AI investment. Network Copilot’s flexibility extends across multiple deployment architectures designed for diverse security and compliance regimes: Organizations under strict regulatory requirements may choose an on-premises or air-gapped deployment option with private LLMs on their existing internal GPU hardware. Businesses who value agility may deploy their private cloud on any of AWS, Microsoft, or Google cloud platforms while maintaining complete ownership of their AI infrastructure.

For organizations wanting an ultra-fast and immediate solution, the platform is also available through a GPU-as-a-Service and LLM-as-a-Service offering which eliminates the need to maintain any AI hardware infrastructure.

This allows organizations to make their own decisions about the balance between infrastructure costs, agility and data governance.