Subsea Inspection’s New Boss
IBM recently explained why AI orchestration is important: As AI systems grow more advanced, a single AI model or agent can be insufficient for handling complex tasks. Autonomous systems frequently struggle to collaborate because they are built across multiple clouds and applications, leading to siloed operations and inefficiencies.
AI agent orchestration bridges these gaps, enabling multiple AI agents to work together efficiently and ensuring that sophisticated tasks are run seamlessly.
In practice, AI agent orchestration functions like a digital symphony, says IBM.
The concept is already being envisioned for subsea inspection work.
Professor Eyad Elyan and Dr Thanh Nguyen of Robert Gordon University and Martin Longmuir of AquaTerra recently provided a thought leadership piece published by the National Subsea Centre in Aberdeen on the idea for automating inspection processes and reporting for tubular structures such as caissons and marine piles.
Inspections typically involve numerical and image data, so one computer-vision agent could process imaging data for anomaly detection, while another agent could focus on numerical data analysis for potential failure prediction.
“An orchestrator AI oversees the process, assigning tasks to agents based on their expertise and combining their outputs to ensure cohesive insights,” say the researchers. “For instance, in subsea monitoring, the orchestrator can synchronize imaging agents that identify structural damage with numerical agents analyzing environmental data, ensuring timely and accurate risk assessments.”
A specialized reporting agent could then autonomously generate inspection reports by interpreting outputs from multi-agent systems. It could adapt its tone and level of detail to suit various stakeholders and integrate contextual information such as industry-specific standards or past inspection records.
It could be interactive, and it could learn from expert human engineers. This would provide a robust balance between adaptability and precision, say the researchers.
IBM defines four types of orchestrator:
Centralized orchestration: A single AI orchestrator agent acts as the "brain" of the system.
Decentralized orchestration: AI agents make independent decisions or reach a consensus as a group.
Hierarchical orchestration: Here, AI agents are arranged in layers, resembling a tiered command structure.
Federated orchestration: Independent AI agents collaborate without fully sharing data or relinquishing control over their individual systems.
As AI systems continue to evolve, AI agent orchestration will become increasingly essential for unlocking their full potential.