Release Notes#

Current Release#

Version: 1.0.0
Release Date: June 2026

Features:

  • Initial release of the Agentic Predictive Maintenance (APM) blueprint.

  • Configuration-driven multi-agent pipeline using LangGraph. Adapt to any defect detection use case by editing four configuration files — no code changes required.

  • Four-agent reasoning pipeline: Policy Agent, Analysis Agent, Evidence Agent, and Ticketing Agent run sequentially to analyze detections and generate structured maintenance tickets.

  • Two operating modes: Large Language Model (LLM) mode for AI-generated analysis (using OpenVINO Model Server) and fallback mode for rule-based operation without an LLM service.

  • Real-time video inference via Deep Learning Streamer (DL Streamer) with YOLO-based object detection; DL Streamer publishes detection events over Message Queuing Telemetry Transport (MQTT).

  • SQLite database-backed storage service with Representational State Transfer (REST) API for querying detections and statistics.

  • Web dashboard (React) with live detection feed, run history, and ticket viewer.

  • The storage service and agent service both expose Prometheus metrics.

  • Reference use case: pipeline-defect-detection with four defect classes — Rupture, Deformation, Disconnect, and Obstacle.

  • Data preparation script for downloading and building sample video from a public Kaggle dataset.

  • On-demand “Run Pipeline” trigger: one full detect-then-reason cycle per click — the DL Streamer pipeline runs once over the (finite) source video, then the agent pipeline reasons over exactly the detections that the run produced (an id-based window). Only one run may be in flight at a time; the agent-service rejects concurrent triggers with 409. Live and continuous background detection is planned for a future iteration.

Hardware Used for Validation:

  • 5th Gen Intel® Xeon® processors (CPU-only)

  • Intel® Core™ Ultra processors with Intel® Arc™ GPU (LLM mode)

Known Limitations:

  • Neural Processing Unit (NPU) inference support for the LLM service is experimental and is not validated for all model and configuration combinations.

  • Only the pipeline-defect-detection use case is provided as a reference configuration. Additional use cases require manual configuration file setup.

  • This release does not include a Helm chart for Kubernetes deployment.

  • GPU-specific sizing and performance benchmarks are not published yet.

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