# February 2026
URL: https://docs.datarobot.com/en/docs/release/cloud-history/2026-announce/february2026-announce.html

> Read about DataRobot's new features, released in February 2026.

# February 2026

## February SaaS feature announcements

February 2026

This page provides announcements of newly released features available in DataRobot's SaaS multi-tenant AI Platform, with links to additional resources. From the release center, you can also access past announcements and [Self-Managed AI Platform release notes](https://docs.datarobot.com/en/docs/release/archive-release-notes/index.html).

### Agentic AI

#### Agent Assist

This release introduces [Agent Assist](https://docs.datarobot.com/en/docs/agentic-ai/agent-assist/index.html) ( `dr-assist`), an interactive AI assistant optimized for the development of AI agents. It helps users design, code, and deploy agents through natural conversation—users describe the agent they want, and the assistant helps build it on the foundation provided by the [Agentic Starter application template](https://github.com/datarobot-community/datarobot-agent-application).

Agent Assist integrates with the [DataRobot CLI](https://docs.datarobot.com/en/docs/agentic-ai/cli/index.html) as a plugin and uses the [DataRobot LLM gateway](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/dr-llm-gateway.html) for model access. During the design and code cycle, Agent Assist can outline which tools an agent should call based on the proposed functionality—for straightforward tools, it can implement the tool code; for more complex tools (such as those that consume API tokens or write to a database), it can scaffold the initial file structure for the human-in-the-loop to complete in the editor or development environment of their choice.

Agent Assist can:

- Design AI agents by helping users think through specifications, ask clarifying questions, and produce an agent specification file ( agent_spec.md ).
- Research solutions using file search and analysis (an internal agent can read files, list directories, grep, and glob).
- Code AI agents by loading an existing agent_spec.md , cloning the DataRobot agent template repository, and implementing the agent with file edits and shell commands.
- Simulate an agent from a specification before coding—rehearsal mode lets users try the design interactively to verify the functionality outlined by the specification.
- Deploy agents to DataRobot following the template’s deployment instructions.

#### New and retired LLMs

With this release, OpenAI GPT-5.2 is available through the [LLM gateway](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/dr-llm-gateway.html). As always, you can add an [external integration](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/ext-llm.html) to support specific organizational needs. See the [availability page](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html) for a full list of supported LLMs.

In addition, the following LLMs are retired:

- GPT-4o Mini (retired February 27, 2026)
- Cerebras Qwen 3 32B (retired February 16, 2026)
- Cerebras Llama 3.3 70B (retired February 16, 2026)
- Mistral (7B) Instruct v0.2 (retired February 25, 2026)
- Marin Community Marin 8B Instruct (retired February 25, 2026)

### Data

#### Database connectivity UI now uniform across NextGen

This release implements a standardized user interface when working with data connections across NextGen, providing a more unified experience. This update includes the following areas:

- InRegistry > Data > Add data.
- TheBrowse datamodal in Workbench.
- InAccount settings > Data connections.
- The vector database creation workflow.

Previously, the interface may have been significantly different based on where you accessed database connectivity.

#### Support for Trino connector added to DataRobot

Support for the Trino native connector has been added to DataRobot, allowing you to:

- Create and configure data connections.
- Upload data from Trino into DataRobot.
- Use as an intake source and output destination for batch prediction jobs.

### Predictive AI

#### Incremental learning now supports dynamic datasets

Incremental learning (IL) is a model training method specifically tailored for supervised experiments leveraging datasets between 10GB and 100GB. By chunking data and creating training iterations, you can identify the most appropriate model for making predictions. This release enables support for using [incremental learning](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/ml-adv-experiment.html#configure-incremental-learning) on dynamic datasets of any size, whereas, static datasets must be between 10GB and 100GB.

### MLOps

#### Asset lineage graph view

The Lineage view provides visibility into the assets and relationships associated with a given MLOps artifact. This view is available on a deployment’s overview tab in Console, in the version details for a registered model in Registry, and in the version details for a custom model in Workshop. In each location, the Lineage section helps you understand the full context of the asset—including models, datasets, experiments, deployments, and other connected artifacts—so you can review AI systems and track how assets relate.

The Graph tab shows an interactive, end-to-end visualization of those relationships as a DAG (Directed Acyclic Graph) made up of nodes (assets) and edges (relationships). The asset you are viewing is highlighted with a purple outline. When reviewing edges, solid lines represent concrete, persistent relationships within the platform, such as a registered model used to create a deployment.Dashed lines indicate relationships inferred from runtime parameters. Arrows generally flow from the "ancestor" or container to the "descendant" or content (for example, from a registered model version to a deployment).

For details on the graph and the available controls, see the [deployment overview](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-overview/nxt-overview.html#lineage), [registered models](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-view-manage-reg-models.html#view-asset-lineage), and [custom model versions](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-custom-model-versions.html#view-asset-lineage) documentation.

#### OpenTelemetry metrics and logs

The [OTel metricstab](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-otel-metrics.html) provides OpenTelemetry (OTel) metrics monitoring for your deployment, visualizing external metrics from your applications and agentic workflows alongside DataRobot's native metrics. The configurable dashboard can display up to 50 metrics. Metrics are retained for 30 days before automatic deletion. Search by metric name to add metrics to the dashboard through the customization dialog box. After selecting the metrics to monitor, fine-tune their presentation by editing display names, choosing aggregation methods, and toggling between trend charts and summary values. OTel metrics can be exported to third-party observability tools.

The DataRobot OpenTelemetry service collects OpenTelemetry logs, allowing for deeper analysis and troubleshooting of deployments. The [Logstab](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-otel-logs.html) in the Activity log section lets users view and analyze logs reported for a deployment in the OpenTelemetry standard format. Logs are available for all deployment and target types, with access restricted to users with "Owner" and "User" roles. The system supports four logging levels (INFO, DEBUG, WARN, ERROR) and offers flexible time filtering options and search capabilities. Logs are retained for 30 days before automatic deletion. Additionally, the OTel logs API enables programmatic export of logs, supporting integration with third-party observability tools. The standardized OpenTelemetry format ensures compatibility across different monitoring platforms.

For details, see the [OTel metrics](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-otel-metrics.html) and [Logs](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-otel-logs.html) documentation.

#### Scaled to zero prediction service improvement

This release increases the chat completion prediction service wait timeout to improve reliability for agentic workflow and custom model deployments using "scale to zero" optimization. When a deployment scaled to zero receives its first prediction request, a new server must be provisioned. The previous 20-second wait timeout was often too short for a new server to become ready, resulting in a "bad gateway" response. This update increases the prediction service wait timeout from 20 seconds to 300 seconds (5 minutes), mitigating the occurrence of "bad gateway" responses when the initial server provisioning takes longer than 20 seconds.

### Platform

#### View resource usage information for your account

All users can now view resource usage information in account settings, providing greater visibility into graphics processing unit (GPU), central processing unit (CPU), and large language model (LLM) API usage across the platform. To access usage information, open [Account settings > Usage Explorer](https://docs.datarobot.com/en/docs/platform/acct-settings/acct-usage-explore.html). From this page, you can view resource consumption by service for a given date range, as well as export the report as a CSV file. Administrators can access an additional dashboard from Admin settings > Tenant Usage Explorer (previously named “Usage Explorer”).

#### OAuth for Google Drive support

This release streamlines DataRobot's OAuth connection process to services like Google Drive and Confluence by introducing a centralized, self-service OAuth system. This means you only have to set up and authorize your external account once, managing all your secure connections in a single spot. DataRobot then automatically retrieves temporary access tokens when needed to ingest your data. This standardization makes connecting easier and more secure, and it will enable these connectors to be used in more DataRobot areas like Apps and Model Creation Projects. For information on how to configure OAuth connection for the providers supported, see [OAuth provider management](https://docs.datarobot.com/en/docs/platform/acct-settings/manage-oauth.html).

### Code first

#### Python client v3.13

Python client v3.13 is now generally available. For a complete list of changes introduced in v3.13, see the [Python client changelog](https://docs.datarobot.com/en/docs/api/reference/changelogs/py-changelog/index.html).

#### DataRobot REST API v2.42

DataRobot's v2.42 for the REST API is now generally available. For a complete list of changes introduced in v2.42, see the [REST API changelog](https://docs.datarobot.com/en/docs/api/reference/changelogs/rest-changelog/index.html).

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