Q2 2026 Product Release: Democratizing Industrial AI

Feature

Problem (Challenge)

Solution: What You Can Do Now

Cognite Flows -  Custom Apps

Production

Rapidly scaling use cases across your enterprise

Industrial builders previously struggled to rapidly deploy and scale tailored enterprise applications inside CDF without creating security flaws or running up massive operational debt.

Subject Matter Experts can leverage a robust, secure, and production-grade environment to deploy custom applications directly inside CDF. Features include an upgraded command-line interface tool and a strict two-step validation safety procedure (deploy and activate) to ensure flawless code-signing. Documentation 

Cognite Flows - Adaptive Experiences

Private Preview

Accelerated time to value with your use cases 

Every single system user used to inherit the exact same uninspiring home landing page, leaving critical site-specific knowledge siloed in workers' heads.

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.Documentation

Cognite Flows - Application Certification

Production

Rapidly scaling use cases across your enterprise

There was no formal certification process for custom applications. Teams could deploy apps without any structured validation, creating risk around data model integrity, security, and production readiness, with no consistent bar across the builder ecosystem. 

Applications undergo a verification process before deployment. Automated validation covers data model integrity, app robustness, security, and performance at scale, while selective human review handles higher-risk scenarios. Certified builders follow a verified path, ensuring every app is safe to operate in industrial environments. Documentation

Cognite Flows - New Developer Experience

Production

Accelerated time to value with your use cases

Industrial Canvas - Symbol Library Update

Production

Increase industrial workforce capacity 

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

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. Documentation

Industrial Canvas- Unified Project Settings for Canvas and Search 

Production

Accelerated time to value 

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

Maintain a single, consistent story for your core data model (CDM) content. Cards in Canvas now reflect the exact same properties as Search, matching your Project Settings configuration perfectly. Furthermore, file connections and annotations now display user-friendly names and descriptions instead of confusing system IDs. Documentation

InField - Field-Ready Observations Mobile Lists

Production

Increase industrial workforce capacity

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.

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

Search UI - Precision Results

Production

Increase industrial workforce capacity

Broad keyword searches historically evaluated entries using a loose "OR" script, flooding engineers with irrelevant records when querying precise tag strings.

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

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Search UI - Download Individual and List of Instances (CSV) 

Production

Accelerated time to value 

Domain specialists lacked a simple, zero-code mechanism to extract curated, filtered data models or associated relationships out of the platform for localized evaluation. 

Download data lists from Search screens directly into a localized CSV file layout. Extracted tables fully capture user-configured column orders, filtered constraints, sorting layouts, and explicit download timestamps. Documentation 

Feature

Problem (Challenge)

Solution: What You Can Do Now

Data Workflows - Native JSON Mapping Tasks

Private Preview

Accelerated time to value

Normalizing data structures or reshaping payloads between individual steps in a workflow traditionally demanded heavy, custom-coded external scripts.

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. Documentation

Function- Access Scoping on Execution Level

Production

Reduce the amount and cost of downtime 

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.

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.

Function Apps Integrated in Workflows (UI) 

Private Preview

Accelerated time to value

Teams could not utilize versioned Function Apps as a first-class, selectable task type within the Workflows UI canvas, limiting onboarding automation velocity. 

Execute Function Apps as native tasks directly within the drag-and-drop Workflows UI editor. Built on top of CDF Functions infrastructure, this packaging model exposes strongly typed parameters, specific path methods, and versioned compute configurations. 

Hosted Extractors: Email Alerting API 

Public Preview

Reduce the amount and cost of downtime 

Hosted extractors lacked an integrated notification layer to match on-premises setups, forcing data engineering teams to manually poll pipeline statuses to detect job failures. 

Proactively configure automated notification sinks and alert subscriptions for specific extraction jobs via the Signals API. Teams automatically receive email notifications when a hosted REST extractor enters a failure or error state. Documentation 

Integrations - API & UI 

Private Preview

Increase industrial workforce capacity 

Traditional extraction pipelines required data engineers to manually access physical VMs to restart service tasks whenever remote configuration files were updated. 

Manage remote connectors using a single, unified Integrations surface that automatically pulls configuration adjustments and pushes live statuses without manual restarts. Provides task-level visibility into run history, errors, and system health. Documentation 

Cognite Connector - Simulation Run Load Balancer

Public Preview

Increase industrial workforce capacity 

Simulator routines were hard-bound to specific connector pipelines, triggering massive backlog timeouts during peak industrial usage periods.

Routes are dynamically queued and picked up by any healthy, available connector. Scale your simulation throughput horizontally by inserting new backend connectors without touching original routine layouts. Documentation

Functions -  Support for Private Link on GCP Clusters 

Production

Reduce the amount and cost of downtime 

Enterprise customers utilizing Private Link on Google Cloud Platform (GCP) clusters for strict network isolation were unable to route traffic from Cognite Functions across the hyperscaler backbone. 

Configure Cognite Functions traffic routing to natively utilize Private Link architectures on GCP setups. This ensures that all automated computing traffic remains on protected network paths, eliminating public internet interception risks.

Data Workflows- References in Workflows 

Production

Accelerated time to value 

Defining dynamic variables inside the data workflows editor was prone to errors, forcing users to type out long, highly specific syntax links manually from memory. 

Assemble complex dynamic parameter strings error-free using a visual Reference Picker directly inside the workflow canvas. The UI automatically populates eligible input and output references for Transformation, Function, and Agent tasks. Documentation 

Python SDK - Asynchronous Concurrency

Production

Increase industrial workforce capacity

Handling massive batch operations asynchronously inside local workspaces required developers to manually script complex concurrency patterns.

Upgrade seamlessly to Python SDK version 8, unlocking fully integrated async client features out of the box. The update optimizes concurrent data downloads and delivers significantly faster file uploads on Windows machines. Documentation

3D - Industrial Street-View Spatial Inspections

Production

Increase industrial workforce capacity

Verticals like discrete manufacturing and pharma often have detailed indoor environments lacking heavy 3D CAD files, making traditional 3D engines feel flat and unnatural. 

Walk through plants using an advanced 360° image walkthrough system. The engine introduces depth cursoring over underlying point clouds, ground-level markers to prevent view blockage, and automatic snapping to the closest capture view. Documentation 

3D - Forced CAD Node Rendering

Private Preview

Reduce the amount and cost of downtime 

Dynamic viewport engines stripped down fine-grain geometric detailing during wide facility loads, removing structural visibility for field design reviews.

Explicitly target up to 100,000 critical structural nodes to remain fully rendered at high detail across all viewport zoom distances. This prevents visual rendering errors during sensitive site construction evaluations.  Documentation 

Feature

Problem (Challenge)

Solution: What You Can Do Now

Atlas AI: Data Analysis (Code Execution) 

Public Preview

Rapidly scaling use cases across your enterprise 

Context caps limited assistant queries to small, 100-row batches, causing agents to drop analytical history threads mid-conversation.

Atlas AI can write, test, and run internal scripts against massive datasets up to 5,000 objects simultaneously. The system remembers the conversational state across iterative questions to build unified statistical trends. Documentation 

Atlas AI - Reusable Domain Skills Packages

Production

Accelerated time to value 

Domain knowledge and specific root-cause analysis logic had to be configured by hand inside separate agent prompts, making configurations bloated and patterns un-reusable. 

Package specific domain expertise into reusable, lifecycle-managed assets called Atlas Skills. Builders can rapid-assemble virtual assistants by composing existing skills instead of starting from scratch.  Documentation

Atlas AI - Query Tool

Production

Accelerated time to value 

Agents searching through extensive graph spaces required deep upfront schema maps, slowing responses and trapping agents on pre-defined paths.

Deploy an intelligent query tool that completely auto-discovers graph data models, inspects properties, and reads available views independently without manual pre-configuration. Requires updating agent runtime to V3. Documentation 

Atlas AI - Unified Agent Experience Across Apps 

Production

Increase industrial workforce capacity

AI assistants behaved differently across individual apps, forcing users to constantly re-learn chat commands depending on their screen view.

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

Agent as Task Type in Workflows 

Private Preview

Rapidly scaling use cases across your enterprise 

Currently there is no provision to execute agents from workflow which limits users to operationalize and scale their use cases. 

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