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Prioritize the right processes. Measure the results. Keep the source code.

Digital Worker 12 AI Agents Example

AI-Powered Emergency Dispatch Orchestration System

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation.

12 AI Agents
1 Tech Stack
AI Orchestrated
Scoped Availability
Worker ID: Emergency Response Optimizer

Problem Statement

The challenge addressed

Traditional dispatch involves manual lookups, phone calls, subjective decisions leading to delayed response, suboptimal matching, missed SLAs, higher costs, and poor customer experience.

Solution Architecture

AI orchestration approach

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Deploys 12 AI agents analyzing IoT data, weather, ML predictions, compliance, TOPSIS selection, A* routing, Monte Carlo SL...
Interface Preview 4 screenshots

Illustrative preview (sample data, not verified outcomes): Emergency Response Configuration Dashboard

Illustrative preview (sample data, not verified outcomes): Real-Time AI Agent Execution

Illustrative preview (sample data, not verified outcomes): Dispatch Decision Summary

Illustrative preview (sample data, not verified outcomes): TOPSIS Decision Matrix and Recommendations

Multi-Agent Orchestration

AI Agents

Specialized autonomous agents working in coordination

12 Agents
Parallel Execution
AI Agent

AI Dispatch Coordinator

Emergency response requires coordinating multiple data sources and decision factors in real-time.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Plans optimized workflow across 12 agents, determines parallel vs sequential execution, manages inter-agent communication, tracks progress, synthesizes final dispatch decision with confidence scoring.

EXAMPLE #1
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AI Agent

Emergency Data Processor

Emergency requests contain unstructured information needing validation and context enrichment.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Validates against property database, enriches with contract/history/IoT data, uses NLP to extract entities, classifies emergency type, assigns priority scores, cross-references sensor data.

EXAMPLE #2
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AI Agent

Real-Time Sensor Analyst

Customer reports may be incomplete; IoT sensors provide objective real-time system conditions.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Retrieves real-time readings for pressure, flow, temperature, vibration. Applies anomaly detection, correlates with reported problem, recommends components for inspection based on sensor signatures.

EXAMPLE #3
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AI Agent

Environmental Impact Analyst

Weather affects travel times and technician safety but is overlooked in traditional dispatch.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Fetches weather conditions and 4-hour forecasts, calculates impact factors for travel, assesses safety, recommends alternate routes, adjusts ETA calculations, provides safety scores.

EXAMPLE #4
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AI Agent

ML Failure Prediction Specialist

Technicians arrive without right parts, requiring return trips that delay resolution.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Runs XGBoost models on historical data to predict parts needed. Checks inventory, calculates costs, recommends items for first-visit resolution, identifies preventive maintenance opportunities.

EXAMPLE #5
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AI Agent

Regulatory Compliance Specialist

Emergency repairs must comply with regulations; technicians need specific certifications.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Identifies applicable regulations (APSAD R5, Code du Travail), determines required certifications, identifies documentation requirements, flags special notifications for ERP buildings.

EXAMPLE #6
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AI Agent

TOPSIS Technician Selection Specialist

Optimal technician selection requires balancing distance, skills, availability, performance, and cost.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Uses ML-Enhanced TOPSIS across 8 weighted criteria: distance (20%), skills (25%), availability (15%), performance (15%), parts (8%), cost (7%), carbon (5%), weather readiness (5%).

EXAMPLE #7
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AI Agent

A* Pathfinding Specialist

Accurate ETA requires real-time traffic, weather, and optimal routing beyond GPS navigation.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Uses A* pathfinding with weather-adaptive heuristics, integrates traffic and weather data, calculates adjustments separately, identifies alternates, provides confidence intervals.

EXAMPLE #8
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AI Agent

Monte Carlo Risk Analyst

SLA commitments have penalties; deterministic calculations don't account for variability.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Runs 15,000 Monte Carlo iterations modeling completion time distribution. Produces SLA probability, expected time, P50/P75/P95/P99 percentiles, risk classification for escalation decisions.

EXAMPLE #9
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AI Agent

Dynamic Programming Cost Specialist

Emergency costs include labor, travel, parts, premiums; organizations miss savings opportunities.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. Uses dynamic programming to minimize cost while meeting SLA. Calculates detailed breakdowns, compares alternatives, quantifies savings. EV selection reduces travel costs 92% vs diesel.

EXAMPLE #10
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AI Agent

ESG & Sustainability Specialist

Organizations must track carbon emissions for ESG reporting and sustainability goals.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Calculates carbon footprint per dispatch option comparing EV vs diesel. Produces emissions in kg CO2, savings percentages, ESG scores (A+ to F), optimization recommendations.

EXAMPLE #11
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AI Agent

Automated Notification Specialist

Customers want immediate confirmation, accurate ETA, and tracking; manual communication is slow.

Core Logic

Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Generates secure tracking links, sends SMS confirmation with ETA and details, sends detailed email, configures automatic updates. Transforms waiting into confident tracking experience.

EXAMPLE #12
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Technical Details

Worker Overview

Technical specifications, architecture, and interface preview

System Overview

Technical documentation

ReAct architecture with RAG integration. 12 agents operate in parallel/sequential phases with reasoning traces, tool execution, and inter-agent messaging for data-driven dispatch decisions.

Tech Stack

1 technologies

WebSocket, IoT/Weather/Traffic APIs, GPS services, XGBoost ML, Monte Carlo engine, TOPSIS, A* pathfinding, SMS/Email notifications

Architecture Diagram

System flow visualization

AI-Powered Emergency Dispatch Orchestration System Architecture
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