# HN812: Nokia EDA: AI Ops You Can Trust (Sponsored)

Page: https://stenobird.com/podcast/heavy-networking/hn812-nokia-eda-ai-ops-you-can-trust-sponsored
Text version: https://stenobird.com/podcast/heavy-networking/hn812-nokia-eda-ai-ops-you-can-trust-sponsored.md
Podcast: [Heavy Networking](https://stenobird.com/podcast/heavy-networking)
Published: 2026-01-30T16:13:14+00:00
Episode link: https://packetpushers.net/podcasts/heavy-networking/hn812-nokia-eda-ai-ops-you-can-trust-sponsored/
Audio file: https://feeds.packetpushers.net/link/12486/17266989/HN812.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/heavy-networking/episodes/hn812-nokia-eda-ai-ops-you-can-trust-sponsored
Duration seconds: 3061

## Resource

Moving beyond AI hype requires a foundation of structured data and multi-vendor abstractions. This episode explores how Nokia's EDA platform uses YANG schemas and event-driven architecture to make AI operations deterministic and trustworthy.

## Highlights
- Main idea: AI in networking fails without a foundation of well-defined schemas and structured telemetry
- Practical takeaway: Use YANG models and network-wide abstractions to provide LLMs with the necessary context for accurate reasoning
- Failure mode: Relying on non-deterministic LLM outputs without enforcing constraints through automation primitives can lead to dangerous hallucinations
- Main idea: An effective AI Ops platform should treat AI agents as interfaces that interact with existing, repeatable automation workflows
- Practical takeaway: Implement an app-based architecture to allow for modular upgrades and extensible network capabilities without system-wide downtime

## Topics

AI Ops, Network Automation, Event-Driven Automation, YANG Models, Multi-vendor Networking, Large Language Models, Network Observability, Infrastructure as Code

## Chapters
- 1:00 — Introduction to Nokia EDA: An overview of Nokia's Event Driven Automation (EDA) platform and its focus on multi-vendor network infrastructure.
- 4:35 — The Power of Abstraction: How using primitives and YANG schemas allows for consistent configuration and status monitoring across different vendors.
- 8:55 — Providing Context to LLMs: The importance of presenting Large Language Models with structured health scores and fabric definitions to ensure accurate reasoning.
- 12:35 — Network-Wide Abstractions: Moving from interface-level configuration to higher-level abstractions like fabrics and services.
- 16:20 — Extensible Data Models: How engineers can implement custom data models and syntax within the automation framework.
- 20:25 — AI Agents and Automation Tools: Defining the relationship between AI chatbots as agents and existing automation workflows as the underlying tools.
- 24:10 — Enforcing Determinism: Using schemas to constrain LLM outputs and prevent hallucinations in production environments.
- 28:15 — Modular Platform Architecture: The benefits of an app-based model for upgrading network capabilities without disrupting the core foundation.

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## Transcript

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