Enterprise AI Analysis
Unlocking Reliable Network Management with MeshAgent
Discover how MeshAgent leverages Large Language Models (LLMs) and a novel constraint-guided workflow to revolutionize network management, ensuring accuracy and reliability.
MeshAgent delivers unparalleled reliability and efficiency for mission-critical network operations.
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Accuracy (excluding abstention)
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Accuracy Improvement
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Constraints (avg. per app)
Deep Analysis & Enterprise Applications
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Introduction
Methodology
Results
Large language models (LLMs) have recently sparked interest in automating network management tasks using natural language interfaces. This paper introduces MeshAgent, a new framework that improves precision by extracting domain-specific invariants from sample queries and encoding them as constraints. These constraints guide LLM's generation and validation process, narrowing the search space and enabling low-effort adaptation.
MeshAgent employs a novel workflow for building specialized and reliable LLM agents. It integrates constraint-guided optimization for reliable code generation and semi-automated constraint creation via failure-driven learning.
MeshAgent consistently improves accuracy across all models, agents, and applications. It achieves over 95% accuracy, reaching 100% when paired with fine-tuned agents, and improves accuracy by up to 26% compared to baseline methods.
95%
Overall accuracy with MeshAgent
MeshAgent Workflow
User Input Query
→
Constraints Creation
→
LLM Generates Code
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Validation & Debugging
→
Confident Output
| Method | Accuracy |
|---|---|
| MeshAgent (CoT+Fine-tuned) |
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| CoT+Few-shot |
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| CoT+Fine-tuned (baseline) |
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Real-World Impact
In a real-world user study with 200 open-ended queries from industry professionals, MeshAgent generalized to unseen tasks with 96% accuracy using constraints built from just 15 seed input/output examples. This highlights its practicality for diverse, unpredictable production environments.
Calculate Your Potential ROI with MeshAgent
Estimate the cost savings and reclaimed hours for your enterprise by implementing MeshAgent's AI-driven network management.
Annual Savings
Hours Reclaimed Annually
Your Path to Intelligent Network Management
Our structured approach ensures a smooth transition to an AI-powered network. Here's what you can expect:
Phase 1: Discovery & Integration
Initial assessment of your current network management workflows and seamless integration of MeshAgent into your existing infrastructure.
Phase 2: Constraint Customization
Collaborative definition and refinement of domain-specific constraints tailored to your unique operational requirements and topology.
Phase 3: Pilot Deployment & Optimization
Deploy MeshAgent in a pilot environment, gather feedback, and iteratively optimize performance and reliability.
Phase 4: Full-Scale Rollout & Continuous Learning
Expand MeshAgent across your enterprise, leveraging its failure-driven learning for ongoing adaptation and improvement.
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