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Data Sources

OPC

SAP

OSIsoft

opentext

MQTT

+90 more

Data Onboarding

Source System Extractors

Data Workflows

Engineering Diagram Parsing

Entity Matching

3D Contextualisation

Functions

Transformations

Industrial Knowledge Graph

Data Modeling

Time Series

Files

Point Clouds

Geospatial

Annotations

Units of Measure

Data Management

Data Explorer

Data Catalogue

Access Management

Atlas AI™

Agent Builder

LLM Benchmarking

Agent Library

Industrial Applications

Field Operations

Industrial Canvas

Charting & Analytics

Maintenance Planning

Consume and Integrate

Grafana

Power BI

GraphQL

REST API

Python

JavaScript

Simulator Connectors

Multi-cloud, Security & Quality

Google Cloud

AWS

Azure

OpenID

ISO 27001

Flows

AI-first industrial
experiences

Native Applications

Charts

Industrial Canvas

InField

Search UI

Custom Applications

Application Certification

Developer Experience

Hosting

Streamlit

Adaptive Experience

Atlas AI™

Industrial agentic
workforce

Document Q&A & Summarization

Industrial Components

Language Model Library & Benchmark

Agent Interaction

Agent Builder

Evaluation Framework

Python Code via Pyodide

Data Fusion®

AI-ready
intelligent data

Data Collection

Extractors

Hosted Extractors

Connectors

Contextualization

Entity Matching

3D Contextualization

Diagram Parsing

Document Parsing

Industrial Knowledge Graph

Data Modeling

Time Series

Files

Records & Streams

Data Products

Search API

Data Management

Data Explorer

Data Catalog

Data Sets

Data Domains

Access & Groups

Image & Video Mgmt

Data Processing & Orchestration

Data Staging (RAW)

Data Workflows

Transformation

Integrations

Functions

Extraction Pipelines

Connect

Open Ecosystem

Partners

AI Developer Tool

Claude

Cursor

GitHub Copilot

Business Apps / Dev Tools

Grafana connector

Excel connection (OData)

Power BI connector (REST)

Jupyter Notebook

Toolkit CLI

REST API

Python SDK

JavaScript SDK

Java SDK

Patterns

Zero Copy Data Lakes

Any Cloud

Microsoft Azure AWS Google Cloud

Release Details

No matching release notes found.

MAR 3, 2026

Simulators UI

What's New:

In-Preview Scenes & 360° Images — View 3D scenes and 360-degree images directly in the search preview pane.
Seamless Transitions — Move from a search preview to the unified 3D world while staying immersed in your 360° view.
Direct Access — Launch full Scenes immediately from the preview card.

Problem:

Users lacked 3D scenes in search previews, leaving them without spatial context for model placement. Navigating to a separate 3D viewer broke the workflow.

Now:

View 3D Scenes and 360° images directly in the search preview to gain immediate context for a selected Location. Opening the full 3D view from a 360° preview even maintains your exact orientation — no more "re-finding" the asset once the viewer loads.

Quick Start:

Prepare — Ensure you are using Scenes for 3D and that they are connected to your Location.
Search for an asset or location in CDF.
Preview the result to see the 3D scene load automatically.
Explore by opening the unified 3D view for full immersion.

Problem:

Unrestricted concurrent runs could lead to unpredictable system behavior or resource contention during periods of high data volume or overlapping schedules.

Now:

Use the new "maximum concurrent runs" setting in Workflow details to ensure consistent execution and better control over how your data processing resources are utilized.

Added Value:

Predictable execution limits provide the stability needed to scale hundreds of concurrent data pipelines across multiple facilities without compromising system performance.

MAR 3, 2026

Workflow monitoring & UI upgrades

Data Workflows New Release Rapidly scaling use cases across your enterprise

Monitor performance at a glance with new Overview columns and deep-dive into failed trigger attempts using the updated Run Details panel and filtering.

Problem:

Limited visibility on the Overview page and a lack of granular detail in execution logs made it time-consuming for engineers to debug performance bottlenecks. Furthermore, trigger failures were not visible at all, leading to "silently" failing workflows.

Now:

Access "Last run" and "Run status" columns directly in the Overview for at-a-glance health checks. The Run history list now explicitly includes "Failed trigger attempts" to eliminate silent failures. The updated Run Details panel provides comprehensive metadata, including trigger status, error messages, trigger type, and the identity of the user or service account that initiated the run. A new "Filter" button allows users to quickly find specific information by time range, workflow version, and run status.

Billions of high-volume log entries and events overburdened the Knowledge Graph node structure, limiting scalability.

Now:

Records API — store billions of structured records with seamless integration to Data Modeling. This unlocks the next level of CDF scalability.

Documentation →

DEC 2025

Full-field production optimization

Beta Workflows

Current CDF capabilities couldn't handle optimizing entire offshore fields with thousands of wells simultaneously. Now, run complex simulations across entire facilities, handling thousands of parameters and unlocking true field-wide production optimization.

Atlas AI New Release

Lack of visibility into agent performance or failures made it hard to build trust or troubleshoot. Now, create test sets and run evaluations to view pass/fail results, enabling data-driven confidence in agent reliability and smoother UAT.

Why is this valuable:

Moving AI agents from pilot to production requires evidence-based trust. Agent Performance Monitoring provides the data to prove reliability, identify regressions, and satisfy stakeholder requirements for production readiness.

Problem:

Blind Deployment — lack of visibility into agent performance or failures made it hard to build trust or troubleshoot issues.

Now:

Agent Performance Monitoring — create test sets and run evaluations to view pass/fail results. This enables data-driven confidence in agent reliability and supports smoother UAT.

Documentation →

DEC 2025

Precise and predictable agent queries

Atlas AI New Release

Agent queries could result in slightly different data or format, creating uncertainty in production workflows. Now, configure a specific query and lock fields, operations, and output to guarantee critical data is retrieved correctly and consistently every time.

Why is this valuable:

Production workflows demand deterministic output. Locking query configuration eliminates the unpredictability inherent in LLM-generated queries, making agents trustworthy for mission-critical data retrieval.

Problem:

Unpredictable Output — agent queries could result in slightly different data or format, creating uncertainty in production workflows.

Now:

Guaranteed Precision — configure a specific query and lock fields, operations, and output. This guarantees critical data is retrieved correctly and consistently every time.

Documentation →

DEC 2025

Enhanced Japanese search

Problem:

Long loading times for 360 images interrupted the user flow, interactions in heavy 3D scenes felt laggy, and performance on large datasets fell short of expectations.

Now:

Faster loading for 360 images, improved responsiveness in heavy 3D scenes, and improved performance for large datasets, including contextualization, zoom-to-asset, and queries, make interactions smoother, even in large or heavy 3D scenes.

Added Value:

Engineers stay in flow instead of waiting on loading or laggy interactions, even when working with large or heavy 3D datasets.

Documentation →

SEP 8, 2026

Smarter Search Results: Precision Search

Search Production Increase workforce capacity

Search now breaks queries and data into smaller terms and matches them more strictly, so results are far more precise.

Problem:

Search queries matched too loosely, expecting any part of the query to match rather than all of it, which hurt precision, and matching also handled partial terms poorly.

Now:

Updated /search API logic now breaks both the search query and the underlying data into smaller searchable terms using dashes, dots, whitespace, and capitalization, so searching "unitCode" finds "unit" and "code," and it recognizes that "0059" and "59" are often the same thing, matching with or without leading zeros. Combined with the AND search operator, this delivers more relevant results every time (the operator has to be specified when using the API).

Smarter Search Results: Precision Search

Added Value:

Search returns exactly what you're looking for far more often, whether you're typing a partial tag or a number with or without leading zeros.

Documentation →

SEP 8, 2026

Search Preview Panel Redesign

Access an intelligent landing page that builds a personalized dashboard experience via conversational AI within under 5 minutes from first login. Teams can pin custom widgets to hit an aggressive 50% Daily Active User (DAU) organizational adoption target.

Adaptive Experiences — AI-powered personalized home dashboard

Added Value:

Cuts typical platform onboarding from weeks to minutes, driving higher daily active usage by meeting each user with the exact operational information they need from their very first login.

This feature is currently in Private Preview. Reach out to your Cognite contact to learn more or request access.

MAY 26, 2026

Symbol Library Update

Industrial Canvas Production Increase workforce capacity

Over 300 new standard industrial symbols organized by category, with proportional resizing and persistent color memory for faster work package creation.

Problem:

Field operators editing Piping and Instrumentation Diagrams (P&IDs) lacked native tools, bottlenecking the creation of clean digital work packages.

Now:

Access more than 300 new standard industrial symbols organized cleanly by category for rapid discovery during work package creation. Key workspace updates introduce proportional resizing and persistent color memory to automatically save the last-used shape properties.

Industrial Canvas Symbol Library — categorized P&ID symbols

Added Value:

Empowers field engineers to build compliant, visually rich work packages directly inside CDF without switching to external drawing tools, saving significant time per work order.

Documentation →

MAY 26, 2026

Unified Project Settings

Industrial Canvas Production Accelerated time to value

Canvas cards now reflect the exact same properties as Search, ensuring a single consistent story for your Core Data Model (CDM) content across the platform.

Problem:

When visual presentations drift across different tools, users lose confidence that they are viewing the same modeled reality across the platform.

Now:

Cards in Canvas now reflect the exact same properties as Search, matching your Project Settings configuration perfectly. File connections and annotations now display user-friendly names and descriptions instead of confusing system IDs.

Added Value:

Ensures every team member sees a consistent data representation across the platform, building trust in the digital twin and eliminating reconciliation work between tools.

Documentation →

MAY 26, 2026

Field-Ready Observations Mobile Lists

InField Production Increase workforce capacity

View, filter, and track nearby location-contextual observations directly on mobile devices, eliminating duplicate work reports in the field.

Problem:

Workers in the field could generate new observations on their mobile devices but had no way to track existing entries on the go, resulting in massive duplicate work reports.

Now:

View, filter, and track nearby location-contextual observations directly on mobile devices. This rollout transitions core field workflows to a unified industrial data model.

Evaluate declarative expressions directly against JSON inputs inside the core Data Workflows canvas. Users can apply quick transformations, string filters, and custom aliases natively without leaving the workflow interface.

Data Workflows — JSON Mapping task configuration panel

Data Workflows — Function Apps as native task type in UI

Added Value:

Eliminates external scripting dependencies for common data normalization tasks, cutting workflow development time and making it easier for non-engineers to build robust, production-ready data pipelines.

This feature is currently in Private Preview. Reach out to your Cognite contact to learn more or request access.

MAY 26, 2026

Access Scoping on Execution Level

Functions Production Reduce downtime

A standalone execution-only access level that cleanly separates operational execution from data modification roles.

Problem:

Execution privileges were historically hard-tied to broad data write permissions, forcing teams to grant overly permissive access roles just to run pre-approved tasks.

Now:

Grant explicit execution-only privileges through a standalone access level control. This cleanly separates operational execution from data modification roles, protecting your underlying data pipelines.

All core search surfaces across CDF front-ends (Search, Canvas, Charts, 3D, and InField) have migrated to an implicit AND operator. Searching strings like "21-PT-10-19" now demands that all tokens match, dramatically improving query precision.

Atlas AI agents now behave consistently across all CDF applications. Execute configured AI agents as native steps within your data processing pipelines, allowing data teams to feed transformation output directly into an agent prompt for advanced processing.

Atlas AI Unified Experience — agent runtime V2 vs V3 comparison

Added Value:

Eliminates the friction of switching between different AI interfaces across CDF applications, enabling every user to leverage AI assistance regardless of which screen they are working from.

Documentation →

MAY 26, 2026

Agent as Task Type in Workflows

Private Preview Rapidly scaling use cases

Execute Atlas AI agents as first-class native task steps directly within Data Workflows pipelines.

Problem:

There was no provision to execute agents from workflows, limiting teams' ability to operationalize and scale AI use cases.

Now:

Execute configured AI agents defined through Atlas AI as native steps within your data processing pipelines. Data teams can feed the direct output of a transformation task into an agent prompt for advanced processing.

Added Value:

Bridges the gap between AI reasoning and automated data pipelines, enabling organizations to operationalize AI-driven decisions at scale within their existing workflow automation infrastructure.

This feature is currently in Private Preview. Reach out to your Cognite contact to learn more or request access.