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Roadmap

This roadmap outlines the future direction of TDengine IDMP, helping users plan long-term adoption and integration.

Features on the roadmap are grouped by delivery cadence into three categories:

  • Now: Capabilities currently under development and being delivered in upcoming releases.
  • Next: Planned directions that will be started after the Now items.
  • Later: Longer-term strategic directions still being researched and refined.

This roadmap reflects our current plans and direction. It may evolve based on customer feedback and market changes. Actual release timing and scope are confirmed in the official release notes.

Product Themes​

  • AI Capabilities: Deeply embed AI into every scenario so that it becomes an intelligent assistant for every IDMP user.
  • Advanced Analytics: Continuously expand analytical capabilities, event analysis, and model capabilities to help users extract deeper business insights from industrial data.
  • Capability Enhancement: Strengthen data modeling, external data referencing, industrial ontology, data quality, and document knowledge management to make the platform more complete and powerful.
  • User Experience & Enterprise Readiness: Continuously improve the user experience and strengthen security, compliance, availability, and maintainability to meet the production deployment requirements of medium and large enterprises.

Now​

Capabilities being delivered in upcoming releases, expected to ship in batches from Q3 to Q4 2026.

AI Capabilities​

  • Multi-model registration and intelligent routing: Register multiple LLMs and route requests by scenario, selecting the model with the best balance of cost and effectiveness.
  • AI Function expansion: Standardize and extend the AI Function interface so that AI can invoke more system capabilities and iterate on complex in-system tasks through dialog.
  • Copilot expansion: Extend Copilot assistance to more pages, bringing AI into more front-end workflows.
  • Multi-agent and AI workflows: Introduce Multi-Agent orchestration and AI workflow composition so that AI can collaborate on complex tasks, with agent scheduling and reasoning recorded.
  • AI-powered personalized pages: Generate personalized pages for every user quickly through AI.
  • Page-type AI analysis: List pages provide overview and descriptive analysis, while specific pages provide deep-dive insights.
  • IM channel expansion: Expand IM channels such as Slack, WhatsApp, and Line, with rich-media interactions.
  • AI observability: Establish user-level AI observability metrics covering task success rate, latency, per-user cost and token consumption, skill/tool invocation counts, and fallback and rollback rates.
  • AI performance and quality benchmarks: Establish a benchmark framework to continuously compare latency, cost, and quality across models.
  • AI security controls: Strengthen filtering and blocking of sensitive words, sensitive documents, and critical data; enforce fine-grained task permissions for agents so that every task runs within its authorization boundary.
  • AI performance and testing: Optimize AI performance and complete end-to-end functional, stress, and reliability testing.

Advanced Analytics​

  • Event association analysis: Discover which events frequently co-occur in historical records and quantify the strength of their correlation, providing a foundation for predictive maintenance.
  • Document management and knowledge graph: Improve classification, permissions, and status management for uploaded documents; integrate a knowledge-graph framework that automatically extracts entities and relationships from documents to build a knowledge graph, supporting more complex and flexible knowledge queries embedded in AI Chat and AI Functions for more accurate AI analysis.
  • Analysis workbench enhancements: Enrich analysis configuration options, including custom lane ordering and property configuration, draggable and scrollable workbench pages, and copy, save-as, and export capabilities.
  • Cross-panel filtering linkage: Link filtering across multiple visualization panels so that a selection in one panel drives filtering in others, enabling coordinated drill-down and comparison.
  • KPI in visualization panels: Bring KPI module metrics into visualization panels to enrich KPI presentation.
  • Downsampling decoupling and performance optimization: Decouple downsampling from other analysis functions and optimize performance for smooth rendering of large datasets.
  • Batch analysis workbench: Build a dedicated workbench for batch analysis that incorporates process recipes, batch recipes, and Gantt charts.
  • Model development and management enhancements: Add more machine-learning algorithms and model import/export to TDmodel; extend clustering from X/Y dimensions to higher dimensions with element-level clustering; and expand anomaly detection from point-level outliers to multivariate, attribute-curve, and element-level anomalies.

Platform Capabilities​

  • Asset modeling rework: Switch data-modeling upload templates from CSV to Markdown for easier authoring; make element templates optional so that elements can be created without templates; support parallel upload of multiple data files so that complex modeling tasks can be split across teams; streamline UOM management configuration; and significantly improve back-end modeling performance.
  • External data reference management: Support external databases such as MySQL, PostgreSQL, and InfluxDB; use federated queries to reference time-stamped external data as TSDB virtual-table metrics; convert external relational data into IDMP tag attributes with dynamic updates; enable tag-based querying, filtering, and linked analysis; and allow element limits to dynamically reference continuously changing external data.
  • Event recipes and batch events: Drawing on ISA-88 and PI Event Recipe concepts, let users define multi-stage, multi-step batch event hierarchies and support bulk import, automatic generation, and flexible analysis of complex batch events.
  • Event tag linkage: Based on external data referencing, bring external relational data into events as tag information, supporting flexible filtering and linked analysis.
  • Sub-event management enhancements: Strengthen sub-event management in real-time analysis with nested multi-level sub-events and advanced SQL support.
  • Industrial ontology enhancements: Strengthen network-relationship management in the industrial ontology by visualizing element relationships as graphs with front-end editing; embed relationship graphs in dashboards as visualization panels and expose richer query interfaces for more targeted AI analysis.
  • Data quality management: Establish a data-quality monitoring and assessment framework that defines data standards and governance rules, continuously monitoring the data-processing pipeline across six dimensions: completeness, uniqueness, timeliness, validity, accuracy, and consistency; provide real-time visibility into data-collection status, automatically detect and alert on quality issues, and continuously improve data quality through reporting and closed-loop governance.

User Experience & Enterprise Readiness​

  • User experience and usability: Add quick filtering to all list pages and persist per-user UI configuration in the browser; streamline UOM management, support PDF export for shares, and add WPS support to the Excel Add-in; introduce more panel types and UI components and embed the analysis workbench in the canvas environment.
  • Mail relay server: Add an IDMP mail relay server for notification and alert delivery in intranet environments.
  • High-availability cluster performance: Build on existing multi-instance load balancing and failover to further improve HA cluster performance and stability.
  • License management enhancements: Improve the usability of the TDengine license management module and the security and availability of license services to keep license data safe and reliable.

Next​

Directions planned after the Now items.

AI Capabilities​

  • Deeper autonomous agents: Explore end-to-end autonomous orchestration and execution of complex business workflows by agents.
  • Industry knowledge accumulation: Explore turning industry knowledge and best practices into reusable AI capabilities.

Advanced Analytics​

  • Themes and style customization: Theme selection and finer-grained style customization for panels and dashboards.
  • Third-party chart plugins: An open plugin mechanism for integrating third-party visualization components.
  • Expanded panel configuration: Richer configuration options for legends, series, axes, and more.

Capability Enhancement​

  • Deeper data governance: Building on data quality management, explore capabilities such as data lineage and data catalogs.
  • Broader data ecosystem access: Continue to expand the forms of external data sources and third-party system integration.

Enterprise Readiness​

  • System observability: Expose system-level observability metrics for integration into enterprise monitoring stacks.
  • Globalization and additional languages: Expand language coverage to serve a broader global audience.

Later​

Longer-term strategic directions still being explored.

  • Mobile access: A mobile client for viewing monitoring, events, and analysis anywhere, anytime.
  • More industrial AI capabilities: Continue to expand domain-specific AI for process parameter optimization, predictive maintenance, quality root-cause analysis, and more.
  • Broader ecosystem integration: Deeper interoperability with mainstream industrial software, data platforms, and cloud services.