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.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
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
AI Agents
Specialized autonomous agents working in coordination
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.
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.
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.
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.
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.
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.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization