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AI Change Impact Analysis System

Deploys an 11-agent AI system that performs multi-dimensional impact analysis including dependency mapping, security assessment, compliance validation, risk quantification, performance prediction, digital twin simulation, supply chain analysis, and sustainability assessmentโ€”synthesizing findings into comprehensive reports with approval recommendations..

11 AI Agents
8 Tech Stack
AI Orchestrated
24/7 Available
Worker ID: ai-change-impact-analyzer

Problem Statement

The challenge addressed

Proposed changes to industrial control systems (firmware updates, configuration changes, security patches) can have cascading effects across interconnected devices. Without comprehensive impact analysis, changes risk production disruptions, security...

Solution Architecture

AI orchestration approach

Deploys an 11-agent AI system that performs multi-dimensional impact analysis including dependency mapping, security assessment, compliance validation, risk quantification, performance prediction, digital twin simulation, supply chain analysis, and s...
Interface Preview 4 screenshots

Change request submission interface for critical security patch analysis with device details, vulnerability data, and 11-agent ensemble

Live multi-agent analysis execution showing real-time agent reasoning, tool invocations, and dependency mapping across 6 workflow phases

Comprehensive analysis results with approval decision, risk assessment, and multi-dimensional impact reports for stakeholder review

ESG sustainability analysis showing 72/100 score, carbon footprint reduction of 10%, and energy savings of $288/year

Multi-Agent Orchestration

AI Agents

Specialized autonomous agents working in coordination

11 Agents
Parallel Execution
AI Agent

Orchestrator - Lead Coordinator

Complex change impact analysis requires coordinating multiple specialist agents across sequential phases while synthesizing diverse findings into coherent recommendations.

Core Logic

The Orchestrator manages the end-to-end analysis pipeline across 6 phases: Initialization, Discovery, Deep Analysis, Advanced Intelligence, Planning, and Synthesis. It distributes tasks to specialist agents, aggregates findings, resolves conflicting assessments, calculates overall risk scores, and generates final approval recommendations with confidence levels and conditions.

ACTIVE #1
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AI Agent

Atlas - Dependency Analyst

Industrial systems have complex device interdependencies across production zones, network segments, and control hierarchies that must be understood before implementing changes.

Core Logic

Atlas maps device dependencies using graph analysis of device inventories and network topology scans. It queries device databases, analyzes protocol connections (OPC UA, Profinet, Modbus, EtherNet/IP), identifies critical paths and circular dependencies, and calculates impact propagation across production zones. Tools: query_device_inventory, scan_network_topology.

ACTIVE #2
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AI Agent

Sentinel - Security Analyst

Changes to industrial systems can introduce or remediate security vulnerabilities. Security assessment must identify CVEs, evaluate threat exposure, and validate security posture.

Core Logic

Sentinel performs comprehensive security assessment by querying CVE databases for applicable vulnerabilities, analyzing protocol security (encryption, authentication), calculating CVSS scores, identifying exploit availability, and recommending mitigations. Integrates threat intelligence for active campaign awareness. Tools: fetch_cve_database, analyze_protocol_traffic, query_threat_intelligence, analyze_attack_surface.

ACTIVE #3
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AI Agent

Auditor - Compliance Analyst

Industrial changes must comply with regulations (IEC 62443, NIST CSF, FDA 21 CFR Part 11) to maintain certifications and avoid audit findings.

Core Logic

Auditor validates changes against compliance frameworks by checking applicable requirements, identifying gaps, assessing severity of violations, and recommending remediation actions. Generates compliance reports with requirement-level status and audit trail documentation. Tools: check_compliance_rules.

ACTIVE #4
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AI Agent

Oracle - Risk Analyst

Change decisions require quantified risk assessment that considers multiple factors including likelihood, impact, and mitigation effectiveness.

Core Logic

Oracle performs multi-dimensional risk quantification using Monte Carlo simulation (10,000 iterations) and Analytic Hierarchy Process (AHP) analysis. It calculates success probability, impact distributions, risk scores across categories (security, operational, compliance, financial, technical), and generates risk matrices with mitigation recommendations. Tools: run_risk_simulation.

ACTIVE #5
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AI Agent

Prophet - Performance Analyst

Changes can affect system performance (CPU, memory, latency, throughput) in ways that impact production efficiency and equipment reliability.

Core Logic

Prophet predicts performance impact using Holt-Winters exponential smoothing on historical metrics. It analyzes baseline performance, forecasts post-change metrics, identifies potential bottlenecks, and calculates performance improvement/degradation percentages with confidence intervals. Tools: predict_performance.

ACTIVE #6
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AI Agent

Validator - Test Engineer

Changes require comprehensive testing to validate functionality, security, performance, and integration. Manual test planning is inconsistent and time-consuming.

Core Logic

Validator generates comprehensive test plans covering functional, security, performance, integration, regression, and compliance testing categories. It determines test coverage requirements, creates automated and manual test cases, estimates execution duration, and identifies critical path tests for smoke testing. Tools: generate_test_cases.

ACTIVE #7
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AI Agent

Navigator - Deployment Strategist

Change deployment requires careful sequencing, checkpoint validation, rollback procedures, and maintenance window scheduling to minimize production impact.

Core Logic

Navigator creates optimized deployment plans using topological sort with constraint satisfaction. It generates step sequences with duration estimates, identifies checkpoints requiring validation, creates rollback procedures, and recommends maintenance windows based on production schedules. Tools: create_deployment_plan.

ACTIVE #8
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AI Agent

Mirror - Digital Twin Specialist

Changes to production systems carry risk. Digital twin simulation allows pre-deployment validation to predict behavior changes without affecting live systems.

Core Logic

Mirror simulates device behavior changes using digital twin technology. It runs post-change simulations comparing baseline vs predicted metrics (CPU, memory, latency, response time, error rate, throughput), identifies potential anomalies, and validates safe deployment with confidence scoring. Tools: simulate_digital_twin, query_real_time_telemetry.

ACTIVE #9
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AI Agent

Nexus - Supply Chain Analyst

Changes may require spare parts, vendor support, or procurement that have supply chain dependencies, lead times, and risks that affect implementation feasibility.

Core Logic

Nexus analyzes supply chain impacts including spare parts inventory status, supplier risk scores, lead times, procurement risks (single-source, geographic, geopolitical), and vendor delivery performance. Identifies inventory shortages that could affect rollback capability and recommends restocking actions. Tools: analyze_supply_chain, check_supplier_risk.

ACTIVE #10
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AI Agent

Gaia - Sustainability Analyst

Organizations must track environmental impact and ESG compliance. Changes to industrial systems affect energy consumption, carbon footprint, and sustainability metrics.

Core Logic

Gaia evaluates sustainability impact including carbon footprint analysis (Scope 1/2/3 emissions), energy efficiency changes, ESG compliance against frameworks (GRI, SASB, ISO 14001), and circular economy metrics. Calculates emission reductions, energy savings, and alignment with Science Based Targets initiative. Tools: calculate_carbon_footprint, assess_esg_impact.

ACTIVE #11
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Technical Details

Worker Overview

Technical specifications, architecture, and interface preview

System Overview

Technical documentation

The AI Change Impact Analyzer is an enterprise-grade multi-agent system for comprehensive change impact assessment in OT/ICS environments. It features parallel agent execution for fast analysis, real-time agent thinking visualization, tool calling with MCP-style architecture, inter-agent messaging for collaboration, digital twin simulation for pre-deployment validation, Monte Carlo risk simulation, supply chain and vendor risk assessment, ESG/sustainability impact analysis, and threat intelligence integration. The system produces executive summaries, technical reports, and business impact assessments with confidence scoring.

Tech Stack

8 technologies

OT device inventory with real-time status

Network topology mapping infrastructure

CVE/vulnerability database integration

Compliance rules engine (IEC 62443, NIST CSF, FDA 21 CFR Part 11)

Digital twin platform for device simulation

Supply chain management system integration

ESG/sustainability metrics tracking

Threat intelligence feed subscriptions

Architecture Diagram

System flow visualization

AI Change Impact Analysis System Architecture
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