Enterprise AI Analysis
Continual Benchmarking of LLM-Based Systems on Networking Operations
The poster outlines a vision for designing a modular benchmarking suite for LLM-based systems in networking operations, specifically for incident management. It proposes a three-stage pipeline to generate, execute, and evaluate LLMs on operational tasks. Key findings include a preliminary evaluation of GPT-4.1, Gemini 2.5-Pro, and Claude 3.7 Sonnet, showing that while a feedback loop improves performance, current systems struggle to reliably resolve issues, highlighting the need for further refinement.
Executive Impact at a Glance
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Max Accuracy (Claude 3.7 Sonnet w/ Feedback)
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Max Time to Resolve (Gemini 2.5 Pro w/ Feedback)
Deep Analysis & Enterprise Applications
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Problem Modeling
Solution Generation
Solution Evaluation
Problem Modeling
The process begins by automatically creating problematic networking scenarios. This involves starting with a fault-free network configuration and injecting faults to simulate disruptions. Formal verification methods, such as Config2Spec, are then used to extract initial and misconfigured network specifications (φ and phi, respectively). This stage aims to capture non-linear causal relationships and broader fault manifestations beyond just the data plane, like BGP Route Leaks, ensuring challenging and meaningful test cases.
Solution Generation
This stage focuses on comprehensively representing the disrupted network using specifications, faulty configurations, and topology graphs, coupled with a high-level problem description. This context is encoded into the LLM prompt, textually representing the problem and network state. The framework supports two base pipelines: Zero-shot (single attempt) and Feedback-Loop (iterative refinement based on prior responses). Future extensions will incorporate more realistic incident management workflows.
Solution Evaluation
The final stage evaluates the solution proposed by the LLM. Config2Spec is again used to extract the specification (φ') of the proposed network configuration. This is then compared with the correct specification (φ). Metrics are defined based on the specification similarity of the desired (φ) and resulting (φ') networks, using standard set-based Intersection over Union. The focus is on the improvement of the similarity metric over the faulty network, and the Time To Resolve (TTR) as an efficiency indicator.
Proposed Benchmarking Framework
I. Problem Modeling: Initial Configuration to Faulty Configuration
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II. Proposed Solution Generation: Network Context Representation & LLM-based Pipeline
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III. Solution Evaluation: Proposed Reconfiguration & Resulting Specification
| Model & Approach | Acc. | Impr. | TTR (s) |
|---|---|---|---|
| GPT-4.1 (Zero-Shot) | 0.371 | 0.207 | 8.65 |
| GPT-4.1 (Feedback) | 0.429 | 0.258 | 71.13 |
| Gemini 2.5 Pro (Zero-Shot) | 0.458 | 0.299 | 156.39 |
| Gemini 2.5 Pro (Feedback) | 0.507 | 0.313 | 483.56 |
| Claude 3.7 Sonnet (Zero-Shot) | 0.488 | 0.263 | 9.10 |
| Claude 3.7 Sonnet (Feedback) | 0.527 | 0.286 | 81.26 |
52.7%
Highest Accuracy Achieved by Claude 3.7 Sonnet with Feedback Loop
Impact of Feedback Loops on LLM Performance
Our preliminary evaluation demonstrates that integrating a feedback loop into the LLM-based solution pipeline significantly improves performance across all tested models. For instance, Claude 3.7 Sonnet's accuracy increased from 0.488 (Zero-Shot) to 0.527 with feedback. However, this improvement comes at a considerable overhead, as evidenced by the increased Time To Resolve (TTR), which soared from 9.10 seconds to 81.26 seconds for Claude 3.7 Sonnet. This highlights a critical trade-off between solution quality and efficiency, crucial for real-world incident management.
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Estimated Annual Savings
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Your Implementation Roadmap
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Phase 01: Discovery & Strategy
Comprehensive assessment of current workflows, identification of AI opportunities, and tailored strategy development.
Phase 02: Pilot & Proof of Concept
Development and deployment of a small-scale AI solution to validate impact and gather initial performance data.
Phase 03: Full-Scale Integration
Seamless integration of AI solutions into existing enterprise systems and comprehensive employee training.
Phase 04: Continuous Optimization
Ongoing monitoring, performance tuning, and iterative enhancement based on operational feedback and new data.
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