- Introduction The explosive growth of artificial intelligence (AI) and large language models (LLMs) has created new opportunities and challenges across enterprise and service provider networks. AI-based systems are poised to transform how networks are operated, managed, and secured. However, as the complexity and dynamic nature of networks increase, so too does the demand for real-time insights, automation, and scalability.
This blog explores how Cisco Provider Connectivity Assurance (PCA) can play a pivotal role in enabling intelligent, AI-assisted network operations. We outline the current challenges, the emerging role of LLMs, and how PCA bridges critical gaps in operationalizing AI in the network domain. Many of the ideas and context are informed by the research in "A Survey on Large Language Models for Network Operations & Management." From Washington University St. Louis.
- The Evolving Role of AI and LLMs in Networking
AI, and in particular LLMs, are beginning to influence every aspect of network lifecycle management, from design and deployment to fault resolution and optimization. The LLMs are especially impactful in the following areas:
- Natural Language Interfaces: Network engineers can interact with LLMs using plain language to generate configurations, troubleshoot issues, and interpret logs.
- Predictive Analytics: ML models can predict failures, performance degradation, and security anomalies before they occur.
- Autonomous Operations: Closed-loop systems powered by AI can automatically remediate issues, enforce policies, and optimize traffic flows.
Despite these advantages, real-world deployment of AI/LLMs for network operations faces challenges such as:
- Limited access to real-time, high-fidelity data
- Difficulty integrating with existing legacy systems
- Lack of transparency and trust in AI decision-making
- Operationalizing AI in Network Operations
To harness the full potential of LLMs and AI in networking, organizations need a clear operational strategy:
- Data Collection: Accurate, real-time telemetry is essential. LLMs depend on structured, labeled, and timely data.
- Contextual Awareness: AI decisions must be based on a deep understanding of both underlay and overlay behavior.
- Explainability: Operational teams need to trust the AI’s outputs. This requires AI to use observable, verifiable input data.
- Integration with Legacy Systems: Networks are rarely greenfield; AI must operate in hybrid environments.
- Closed-loop Automation: AI must not just identify problems, but act on them automatically where possible.
- Cisco PCA: The Bridge to AI-Driven Network Operations
Cisco Provider Connectivity Assurance (PCA) is a platform purpose-built to validate, correlate, and assure the health of critical grade networks. It acts as the connective tissue between AI models and real-world network behavior.
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Challenge Identified in Survey
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How PCA Helps
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Lack of real-time network awareness
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Provides ultra deep visibility into underlay transport health
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Manual error-prone operations
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Automates detection, validation, and root cause analysis of underlay issues
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Integration with legacy infrastructure
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Works across multi-vendor and multi-domain environments, aiding AI in complex networks
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Data for LLM-based decision-making
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Supplies structured, accurate, and timely telemetry and event data
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Need for scalable observability
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Scales across global provider and enterprise WAN environments
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Intent-based management
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Ensures underlay network aligns with overlay intent, enabling proactive assurance
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- Use Cases and Real-World Scenarios
- AI-Led Root Cause Analysis: PCA delivers the telemetry and correlation that allow LLMs to provide intelligent RCA recommendations.
- Closed-Loop Automation: AI can initiate workflows such as rerouting, ticketing, or configuration updates when PCA confirms a transport fault.
- Intent Assurance: When network overlays don’t meet policy or SLAs, PCA can confirm whether the issue lies in the network, feeding actionable insights to LLM-powered orchestration tools.
- LLM Copilots for Network Engineers: PCA acts as a trusted backend for LLMs assisting engineers with contextual awareness, enhancing decision-making.
- Conclusion
As networks evolve and the operational load becomes too great for manual oversight, the convergence of AI/LLMs and advanced assurance platforms like Cisco PCA is inevitable. PCA provides the real-time observability, cross-domain visibility, and trustable data needed to power AI decisions.
By combining the cognitive power of LLMs with PCA's assurance and validation capabilities, organizations can realize a future of autonomous, resilient, and intelligent networks.
References
- Survey on Large Language Models for Network Operations & Management (2023)
