Overview
Optimization Agents are specialized, high-confidence AI tools that leverage custom machine learning (ML) models to solve complex industrial problems.
Who Uses It?
Power Users: Primarily Operators and Engineers who require validated recommendations for process adjustment.
Who Builds It?
Data Science Teams: Sight Machine data scientists spend several days tuning base-level agents to specific client data.
Prompt Engineers: Experts from Sight Machine or partners who refine the agent’s "skills" to ensure high-accuracy responses.
How It Works
Custom ML Models: These agents use models trained on thousands of dynamic parameters to predict outcomes like defects or energy spikes.
Orchestration: The agent acts as an orchestrator, calling specific "Tools" (math/ML engines) to perform calculations rather than doing the math itself.
Validation: Every response is grounded in SME-validated (Subject Matter Expert) logic to maintain high confidence in industrial environments.
Example Use Cases
System-Level Optimization: Real-time recommendations for OEE, scrap reduction, overpack prevention, and energy efficiency.
Root Cause Analysis: Identifying the specific parameters causing current production deviations.
Custom Workflows: Validated Q&A flows delivered through natural language or integrated dashboards.
Delivery & Training
Implementation Time: While basic setup is fast, refining models to high-confidence levels typically takes several weeks to months.
Integration: Delivered via the Omniverse Digital Twin, recommendation dashboards, or custom App Builder interfaces.
Workflow Alignment: The UI is tailored to match the customer’s specific operational workflow.