What's new at Domino

Domino Apps now include draft previews, so authors can test changes in a real App runtime before publishing, without touching the version users are on. Every app version is reproducible and can be restarted on demand. Approval gates sit in front of deployment, so Apps ship only after the sign-offs your policy requires.

Why it matters:

• Iterate draft previews, validating behavior before publishing, without disrupting production.
• Versioning is tied to reproducibility, so every app version is traceable and auditable.
• Govern app publication with approval workflows, so AI applications meet enterprise standards before they ship.

Submit and manage Slurm HPC Jobs directly from within the Domino, using existing commands and job scripts. It's real Slurm underneath, managed by Slinky, so nothing changes about how researchers work. Slurm handles scheduling and enforces the policies and limits admins set, while Domino handles monitoring, and governance, unifying HPC and AI/ML workloads under a single system of record.

Why it matters:

• Submit Slurm HPC Jobs from any Domino Workspace using native Slurm CLI commands
• Track job status, resource usage requested against used, and input and output files in one Jobs view
• Reproduce any past run, because every HPC Job lands in the Domino audit trail with full traceability

Domino offers two ways to classify your work. Tags let you sort items into a structured set of categories and values, kept consistent across your organization. Properties let you attach open-ended details, like dates or IDs, to individual items. Both work across Apps, Projects, Project Templates, Datasets, NetApp Volumes, and Models, and both feed into a single search experience.

Why it matters:

• Tags reflect how your organization structures work, kept consistent through centrally managed controls
• Properties capture free-form details, such as a last-validated date or an internal model ID.
• Find what already exists through global search, filters, and tag browsing

Domino Extensions is a framework to embed domain-specific tools directly inside the UI. Surfacing in project sidebars, dataset views, file action menus, and admin navs, they have full visibility into project state, dataset contents, and user identity and inherit Domino's reproducibility, governance, auditability, and API access out of the box.

Why it matters:

• Users stay in their flow, with the right tool appearing in context instead of in a separate window or app.
• Every extension inherits Domino's enterprise controls automatically, no per-tool security review or extra configuration required.
• Teams can build for any vertical, role, or workflow and deploy directly into Domino, without standing up parallel infrastructure.

Domino workspaces provide persistent, governed context across the full AI lifecycle. Coding assistants ship pre-installed with full access to your project, data, and infrastructure.

Why it matters:

• Everything is captured in the audit trail, with full parameter lineage and reproducibility
• Agents see your project files, datasets, and platform resources.
• Three agents, zero setup: Copilot, Claude Code, and Codex are ready to use the moment you open a workspace.

Domino enables teams to securely host and serve large language models (LLMs) within their own infrastructure, deploying them as high-performance GenAI endpoints.

Benefits:

• Host LLMs inside private infrastructure or VPCs with fine-grained access controls
• Reduce operational costs with efficient model storage and runtime mounting
• Maintain full observability into latency, token usage, and system behavior

Domino introduces framework-agnostic instrumentation for agent-based systems, enabling teams to trace and monitor agent interactions from development through production using a single SDK.

Benefits:

• Capture detailed traces including inputs, outputs, tokens, latencies, and tool calls
• Gain full visibility into agent behavior across complex, multi-step workflows
• Automatically associate traces with prompts, models, datasets, and code versions

Teams can visualize, evaluate, and compare agentic systems with both summary and trace-level detail, using shared metrics and complete configuration lineage to support consistent, repeatable evaluation.

Benefits:

• Compare prompts, models, parameters, and agent configurations side by side
• Drill down from aggregate metrics to individual interaction traces
• Evaluate different frameworks and models in the same experiment

Domino closes the gap between experimentation and deployment by enabling teams to deploy agentic systems directly from evaluation results, with the system of record ensuring what is deployed exactly matches what was evaluated.

Benefits:

• Eliminate manual handoffs and brittle deployment scripts
• Reduce risk by deploying fully evaluated, traceable systems
• Maintain complete lineage and auditability across environments

The same instrumentation used during development continues to operate in production, enabling continuous or scheduled monitoring and evaluation of real-world agent behavior, user feedback, and rapid restoration into a workspace for iteration.

Benefits:

• Monitor production performance with real-time traces and dashboards
• Detect regressions through scheduled evaluations
• Rapidly iterate by reopening production systems with one-click for further experimentation