AI Network Transformation Command Center Digital Worker
Deploys a 9-agent AI command center that orchestrates transformation planning through data discovery, risk assessment, vendor optimization, schedule planning, and compliance validation. Uses RAG pipelines for data enrichment, ML models for risk prediction, and constraint optimization for scheduling.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Program Configuration Interface - Define transformation scope, AI automation levels, risk tolerance, and vendor preferences
Multi-Agent Orchestration - Real-time view of 9 specialized agents processing transformation planning with live reasoning
Data Discovery & RAG Pipeline - Vector search results, semantic queries, and real-time API integrations across carrier systems
Execution Summary - Comprehensive analysis results with identified savings, compliance scores, and actionable recommendations
AI Agents
Specialized autonomous agents working in coordination
Program Orchestrator Agent
Network transformation programs require coordinating multiple workstreams with complex dependencies while ensuring all analyses complete in the correct sequence.
Core Logic
Coordinates multi-agent workflows using workflow_manager, agent_router, and dependency_resolver tools. Establishes inter-agent communication channels, monitors workflow progress, allocates tasks based on agent specializations, and synthesizes results from all analysis agents into coherent program plans.
Data Discovery Agent
Network transformation requires complete site inventory data from multiple sources with varying data quality, formats, and completeness levels.
Core Logic
Discovers and validates inventory data using RAG pipelines. Connects to carrier APIs (AT&T E-Access, Verizon Business), queries internal CMDB, and processes uploaded files. Uses query_inventory_database, analyze_carrier_data, validate_records, and enrich_data tools. Identifies data gaps and suggests enrichment sources.
Risk Intelligence Agent
Site migrations carry varying levels of risk based on complexity, criticality, location, and historical patterns that require predictive analysis to prioritize.
Core Logic
Analyzes risks using ML models trained on historical migration outcomes. Uses calculate_risk_score for site-level predictions, analyze_patterns for failure pattern recognition, and generate_mitigation for remediation strategies. Provides confidence intervals and identifies top risk factors per site.
Vendor Selection Agent
Optimal vendor selection requires balancing performance history, geographic coverage, capacity constraints, and cost across hundreds of sites.
Core Logic
Optimizes vendor allocation using select_vendor based on SLA compliance, analyze_performance for historical metrics, and optimize_allocation for portfolio-wide assignment. Calculates projected cost savings and balances workload to prevent vendor bottlenecks. Generates vendor-site compatibility scores.
Schedule Optimization Agent
Implementation schedules must satisfy resource constraints, site dependencies, vendor capacity limits, and business continuity requirements while minimizing duration.
Core Logic
Generates optimized timelines using constraint satisfaction algorithms. Uses optimize_schedule for timeline generation, resolve_conflicts for dependency management, allocate_resources for capacity planning, and parallelize_tasks for concurrent execution opportunities. Calculates schedule confidence metrics.
Insights Synthesis Agent
Program stakeholders require different views of analysis results tailored to executive, technical, and business audiences with appropriate detail levels.
Core Logic
Synthesizes insights using generate_report for stakeholder-specific documents, create_visualization for charts and graphs, summarize_findings for executive summaries, and format_export for PDF, Excel, JSON, and PowerPoint outputs. Structures content by priority and audience relevance.
Compliance & Governance Agent
Network transformations must maintain compliance with SOC2, GDPR, FCC, HIPAA, and other regulatory frameworks throughout the migration process.
Core Logic
Validates compliance using compliance_scanner for framework assessments, audit_trail_generator for activity logging, policy_validator for configuration checks, and dsr_processor for data subject requests. Tracks certifications, identifies violations, and generates remediation plans with deadlines.
Sustainability & ESG Agent
Network transformations impact carbon footprint through equipment changes, energy consumption, and e-waste generation requiring tracking for ESG reporting.
Core Logic
Tracks environmental impact using carbon_calculator for CO2 emissions, energy_analyzer for consumption patterns, ewaste_tracker for retired equipment, and green_vendor_scorer for sustainable procurement. Calculates net-zero progress, identifies CO2 reduction opportunities through vendor selection, and generates ESG disclosure reports.
Zero-Trust Security Agent
SD-WAN migrations must implement zero-trust architecture, SASE components, and proper network segmentation while maintaining security throughout transition.
Core Logic
Implements security controls using ztna_validator for zero-trust policy verification, sase_integrator for SASE component deployment, threat_detector for vulnerability scanning, and micro_segmenter for network isolation. Validates continuous authentication, configures access policies, and assesses zero-trust maturity scores.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization