Enterprise Crisis Response Digital Worker
Deploys ten specialized AI agents executing a 15-step analysis plan: collecting multi-source data, synchronizing digital twin state, analyzing patterns, determining root causes via Bayesian fault trees, predicting outcomes, generating validated recommendations, tracking ESG impact, and producing executive summariesâall with full audit trails and autonomous action capabilities..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Crisis scenario selection interface with pre-configured scenarios and sensor data configuration for comprehensive multi-agent production analysis
Agent orchestration workflow displaying real-time tool execution flow with OPC-UA data collection and digital twin synchronization
Live agent reasoning trace visualizing 37 thoughts across 10 specialized agents with real-time confidence scoring and decision tracking
Analysis results dashboard showing root cause identification, business impact assessment with $110,000 savings, and prioritized AI recommendations
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Enterprise crisis response requires coordinating ten specialized agents through a complex 15-step analysis workflow while managing dependencies, tracking progress, and ensuring consistent output quality.
Core Logic
Coordinates multi-agent workflow using Claude-3.5-Sonnet at temperature 0.3 with 4,096 max tokens. Manages task delegation through workflow_engine and agent_router tools. Tracks execution across 15 steps from input validation through executive summary compilation. Monitors token usage against 128K limit and aggregates agent outputs.
Data Collector Agent
Crisis analysis requires data from multiple heterogeneous sourcesâreal-time sensors, historical databases, event streamsâeach with different protocols, latencies, and data formats.
Core Logic
Integrates three data collection tools: OPC-UA Client (125ms latency) for real-time sensor data from 847 sensors, TimescaleDB Query (85ms) for historical records from 15,000+ entries, and Kafka Consumer (15ms) for real-time event streams. Validates data quality and handles missing values. Correlates data from CMMS and ERP systems.
Pattern Analyzer Agent
Identifying meaningful patterns and anomalies in high-dimensional sensor data requires sophisticated statistical analysis that can distinguish genuine issues from normal operational variations.
Core Logic
Applies FFT analyzer (250ms) for frequency domain pattern detection, Isolation Forest (180ms) for unsupervised anomaly detection, and statistical tests (45ms) for hypothesis validation. Extracts features from time-series data and classifies anomaly severity. Correlates patterns across multiple sensor streams for comprehensive analysis.
Root Cause Agent
Determining the true root cause of manufacturing issues requires systematic analysis that traces symptoms back through causal chains while quantifying the probability of different failure modes.
Core Logic
Constructs hierarchical fault trees using Fault Tree Builder (320ms) with Bayesian probability calculations. Applies Bayesian Network inference (450ms) for causal analysis. Runs FMEA Analyzer (280ms) for failure mode effects assessment. Outputs root causes with contributing factors, confidence levels, and probability-weighted impact scores.
Predictive Agent
Crisis response requires understanding not just current state but future trajectoriesâhow will the situation evolve, what is the failure timeline, and what are the probabilistic outcomes of different scenarios.
Core Logic
Executes Weibull Analyzer (150ms) for reliability and time-to-failure prediction. Applies Prophet Forecast (380ms) for time-series prediction with seasonality decomposition. Runs Monte Carlo simulation (520ms) with thousands of iterations for risk quantification and scenario probability assessment.
Recommender Agent
Translating analysis findings into actionable recommendations requires balancing multiple objectivesâcost, safety, production impactâwhile optimizing resource allocation and prioritizing interventions.
Core Logic
Applies Resource Optimizer (220ms) using linear programming for optimal resource allocation. Calculates ROI using financial analysis tool (85ms) including downtime costs, labor, and parts. Checks real-time inventory via API (65ms, /api/inventory endpoint). Generates prioritized action plans with cost-benefit rankings and resource assignments.
Validator Agent
Recommendations must comply with enterprise policies, safety regulations, and budget constraints. Unchecked recommendations risk policy violations, safety incidents, or budget overruns.
Core Logic
Executes Policy Checker (95ms) for enterprise policy validation. Applies Safety Validator (120ms) for OSHA protocol compliance verification. Runs Budget Validator (45ms) for financial constraint checking. Filters and adjusts recommendations to ensure all outputs are policy-compliant, safe, and financially feasible.
Digital Twin Agent
Understanding current facility state and simulating the impact of proposed actions requires a synchronized virtual representation that accurately reflects physical reality.
Core Logic
Maintains synchronized digital twin using Twin Sync Engine (45ms, 99.7% accuracy). Runs Physics Simulator (320ms) for equipment behavior modeling. Executes State Predictor (180ms) for future state projection. Tracks sync status (synchronized/syncing/stale/disconnected) and identifies component anomalies. Provides real-time OEE, throughput, and availability metrics.
ESG Monitor Agent
Manufacturing decisions have environmental, social, and governance implications that must be tracked for regulatory compliance, sustainability reporting, and stakeholder accountability.
Core Logic
Calculates carbon footprint using Carbon Calculator (95ms) across Scope 1, 2, and 3 emissions. Analyzes energy efficiency via Energy Analyzer (110ms) including renewable percentage and power factor. Generates ESG Reporter output (150ms) for ISO 14001, EU ETS, and GRI Standards compliance. Produces Environmental (76), Social (85), and Governance (91) scores.
Self-Optimizer & Autonomous Executor Agent
Crisis response speed is critical, but some actions require human approval while others can be safely automated. The system must learn from outcomes to continuously improve its recommendations.
Core Logic
Manages autonomous action queue with confidence-based auto-approval (>98% confidence for low-risk actions) and role-based approval routing for high-impact decisions. Implements feedback learning with 0.03 learning rate and 91.2% model accuracy. Tracks 847 optimized decisions with positive/negative/neutral feedback loops. Applies parameter tuning, model updates, and workflow optimizations based on outcomes.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization