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System Status
Online: 3K+ Agents Active
Digital Worker 6 AI Agents Active

Autonomous Inspection Mission Digital Worker

Implements a ReAct (Reasoning + Acting) multi-agent system that autonomously monitors and controls inspection missions in real-time. Six specialized agents collaborate through structured reasoning (Thought → Action → Observation) to plan trajectories, monitor equipment health, analyze signal quality, optimize parameters adaptively, respond to anomalies, and generate comprehensive reports.

6 AI Agents
6 Tech Stack
AI Orchestrated
24/7 Available
Worker ID: autonomous-inspection-worker

Problem Statement

The challenge addressed

Real-time inspection operations require continuous monitoring, parameter adjustment, quality analysis, and anomaly response. Human operators cannot simultaneously track equipment health, optimize parameters, analyze quality trends, and document resul...

Solution Architecture

AI orchestration approach

Implements a ReAct (Reasoning + Acting) multi-agent system that autonomously monitors and controls inspection missions in real-time. Six specialized agents collaborate through structured reasoning (Thought → Action → Observation) to plan trajectories...
Interface Preview 4 screenshots

AI Agentic Mission Configuration - Setup autonomous inspection mission with target component selection, quality thresholds, and AI Agent Team including Mission Orchestrator, Planner, Analyzer, and Optimizer

Live RPV Weld Inspection - Real-time mission execution at 100% progress showing active agents with ReAct reasoning pattern, tool executions, and equipment health monitoring

RPV Weld Inspection Final Report - Mission completed successfully with component approved for service, 97.8% quality score, 100% coverage, and scenario execution flow visualization

Inspection Success Metrics - $127,500 cost saved, 2.5h time saved, +14.5% quality improvement with industry benchmarks comparison and key findings summary

Multi-Agent Orchestration

AI Agents

Specialized autonomous agents working in coordination

6 Agents
Parallel Execution
AI Agent

Mission Orchestrator Agent

Coordinating multiple AI agents during live inspection operations requires real-time task delegation, conflict resolution, and seamless handoffs between automated actions and human oversight.

Core Logic

Coordinates the inspection mission from initialization through completion using the ReAct reasoning pattern. Manages agent activation sequences, delegates tasks to specialist agents, monitors inter-agent communication, resolves conflicts between competing recommendations, and handles escalation to human operators. Maintains mission state, tracks overall progress, and synthesizes agent insights into actionable recommendations.

ACTIVE #1
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AI Agent

Inspection Planner Agent

Generating optimal scan trajectories for complex component geometries (cylindrical welds, HAZ zones) requires balancing coverage, time efficiency, and equipment constraints.

Core Logic

Creates optimal inspection plans using path planning algorithms optimized for component geometry. Computes helical, raster, or sector scan patterns based on coverage targets. Calculates total path length, estimated completion time, and identifies zones requiring special attention (HAZ regions). Uses tool calls to retrieve reference trajectories and validate coverage projections.

ACTIVE #2
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AI Agent

Quality Analyzer Agent

Continuous quality monitoring during inspection requires real-time signal analysis, anomaly detection, and trend identification that exceeds human operator capabilities.

Core Logic

Performs real-time signal analysis on streaming scan data using ML-powered anomaly detection. Monitors SNR, coupling quality, and amplitude metrics continuously. Detects quality degradation trends, classifies anomaly patterns (defects vs geometric features), and escalates significant findings to the optimizer or orchestrator. Generates quality metrics summaries with confidence scores.

ACTIVE #3
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AI Agent

Parameter Optimizer Agent

Inspection parameters (gain, frequency, scan speed) require adaptive adjustment based on material zones, surface conditions, and real-time quality feedback.

Core Logic

Calculates optimal parameter adjustments using adaptive control algorithms. Responds to quality alerts with compensatory parameter recommendations (gain adjustments, speed modifications, frequency changes). Queries material databases for zone-specific settings, performs parameter tuning optimization, and requests human approval for significant changes per autonomy configuration.

ACTIVE #4
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AI Agent

Equipment Monitor Agent

Equipment failures during inspection cause costly interruptions. Proactive health monitoring and failure prediction can prevent unplanned downtime.

Core Logic

Continuously monitors equipment telemetry (probe temperature, element health, robot motor currents, position accuracy). Tracks probe element response patterns for degradation signs, correlates temperature trends with signal quality, analyzes motor current spikes, and runs predictive failure models. Generates equipment health scores and proactive maintenance recommendations.

ACTIVE #5
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AI Agent

Report Generator Agent

Generating comprehensive inspection reports for multiple stakeholders (executives, engineers, auditors) is time-consuming and prone to inconsistencies.

Core Logic

Generates multi-stakeholder reports including executive summaries with ROI analysis, technical reports with quality metrics and anomaly details, compliance certificates (ASME Section XI, NRC 10 CFR 50.55a, ISO 17640), and data archives. Creates 3D defect map visualizations, compiles agent decision logs, and produces audit-ready documentation with cryptographic signatures.

ACTIVE #6
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Technical Details

Worker Overview

Technical specifications, architecture, and interface preview

System Overview

Technical documentation

The Autonomous Inspection Mission Digital Worker is a ReAct-pattern multi-agent system for real-time inspection operation control. It deploys six specialized agents that use structured reasoning (Thought → Action → Observation) with tool-calling capabilities to autonomously manage inspection missions. The system handles trajectory planning, real-time quality monitoring, adaptive parameter optimization, equipment health prediction, anomaly detection and response, and automated report generation. Configurable autonomy levels (supervised, semi-autonomous, autonomous) allow operators to set appropriate human-in-the-loop controls.

Tech Stack

6 technologies

Real-time data streaming from inspection equipment

Robot controller interface for trajectory execution

Equipment telemetry feeds (temperature, position, motor currents)

Signal processing pipeline for quality metrics

Historical inspection data for ML model inference

Operator interface for human approval workflows

Architecture Diagram

System flow visualization

Autonomous Inspection Mission Digital Worker Architecture
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