# 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.