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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Ship internal applications from your AI Agent with Datadog Apps
Barak Shoushan, Addie Beach, Scott Kennedy · 2026-06-09 · via Datadog | The Monitor blog

AI coding agents have made it much faster to build internal tools. Teams can move from a rough idea to working code quickly, especially for operational apps that support how they release, troubleshoot, and maintain software. As more of these apps move into production, teams need a way to deploy and run them where engineers already work, access data and APIs securely, and share them consistently across teams. 

Datadog Apps gives teams a code-first way to build applications that run inside Datadog. You can easily create these apps via the AI agents of your choice, such as Claude Code, Cursor, and Codex. Your applications inherit the same authentication, access controls, observability, and connections that your team already uses throughout Datadog. Your apps embed directly into dashboards, notebooks, developer homepages, and Self-Service Actions pages, rather than living as isolated tools across disconnected services.

In this post, you’ll learn how you can use Datadog Apps to:

  • Build apps from any agent, IDE, or CI pipeline

  • Connect applications through the Actions platform

  • Ship with built-in governance, security, and observability

  • Embed applications across the Datadog platform

Build apps from any agent, IDE, or CI pipeline

Datadog Apps treats internal applications like production software. Using familiar technologies like Vite and React alongside your preferred agents or IDEs, you can build apps that embed into Datadog without needing to learn a new framework. Because your app lives in code, your team can use the GitHub action to collaborate through Git, review changes in pull requests, and deploy through your existing CI/CD pipeline.

To get started, run npm create @datadog/apps@latest from your development environment to scaffold your app, then spin up a local development server. Once your app is finished, Datadog Apps guides you through uploading it to Datadog. 

The Datadog Apps CLI scaffolding a new internal application inside an AI coding tool, with next steps for deploying a finished application to Datadog displayed.

Datadog Apps includes skills for AI coding agents that teach them how to work with the Datadog platform. Agents can use these skills to generate applications that use Datadog UI components, call backend functions, navigate the platform, and fit within Datadog’s security model. This helps agents generate apps that already account for Datadog’s platform requirements, rather than producing generic frontend code that needs to be reworked later.

Connect applications through the Actions platform

Most internal applications need access to external systems such as databases, cloud services, or SaaS platforms. When each app manages those connections separately, teams end up duplicating credentials, storing secrets in more places, and maintaining inconsistent authentication logic across services.

Datadog Apps connects to your stack through the Actions platform, the same connection layer that powers features like Workflow Automation, Bits Agent Builder, and App Builder. The Actions platform provides connections to cloud providers, SaaS tools, and Datadog accounts as well as authentication methods like OAuth, API keys, and IAM policies. You can reuse existing connections, credentials, and integrations across applications for easy setup. Additionally, you can use the HTTP action to connect your app to any API.

A list of available integrations and reusable secure connections within the Datadog Actions platform, including those for cloud providers, SaaS tools, and API keys.

App code references connections by ID, while Datadog resolves the underlying credentials server-side at runtime. This lets developers call backend functions without putting secrets directly in application code. 

Connection-level permissions mirror existing team access. For example, if only certain teams are authorized to use a sensitive production database, an app connected to that database respects those same boundaries without requiring a separate access model.

Applications created through Datadog Apps can also access data within Datadog Datastore. Datastore provides persistent storage that integrates with the rest of the Datadog ecosystem. You can use the Actions platform to add, delete, list, and update shared Datastore data directly within your application.

Inherit governance, security, and observability

As the number of agent-built applications grows, managing access, investigating activity, and monitoring reliability becomes a real challenge. Separate authentication systems, monitoring instrumentation, and audit logs make each application harder to secure, troubleshoot, and audit consistently.

Datadog Apps inherits governance and observability capabilities directly from the Datadog platform. Every app uses Datadog’s identity model, including existing SSO, Teams, and RBAC configuration, so you don’t need to provision separate user accounts or authentication systems. This keeps app access tied to the same identity model you already use across Datadog.

All user activity within Datadog Apps flows into Datadog’s existing audit infrastructure. Datadog Audit Trail gives you a record of actions taken across infrastructure, dashboards, monitors, and applications. You can also use Audit Trail to help you quickly detect unexpected or potentially risky activity within your applications, enabling you to alert on notable events that could affect cost or security. 

Datadog Audit Trail displaying a unified log of user activity across infrastructure, dashboards, and internal applications, with event details available for security review.

Additionally, you can access high-level visualizations of user activity within your application via the out-of-the-box App Builder Overview dashboard. The metrics on this dashboard give you insight into how often your app is executed, the most popular actions users take, and which dashboards it’s been embedded on, among other data.

The App Builder Overview dashboard, displaying data on the total number of apps within Datadog and the frequency of app executions.

Embed applications across the Datadog platform

Operational applications are most useful when they appear next to the telemetry data and workflows they affect. Datadog Apps lets you embed applications directly into the Datadog features you already use, including dashboards, notebooks, developer homepages, and Self- Service Actions pages. 

A Datadog App application embedded inside a dashboard. The app includes a searchable table that helps teams identify feature flags that need to be cleaned up.

Applications can also appear across multiple features simultaneously. For example, a rollback app might appear on a Self-Service Actions page during routine operations, inside an incident notebook during an outage, and on a developer homepage for on-call workflows. By embedding applications throughout engineering workflows, you can reduce context switching and keep actions close to the systems they impact.

Datadog Apps provides prebuilt components for forms, tables, charts, and modals that match the styling within Datadog’s DRUIDS library. Because applications share design systems and platform context, you don’t need to manage separate theming frameworks or maintain disconnected frontend experiences. Internal tools appear as native extensions of the Datadog platform.

Use Datadog to build operational applications

AI coding agents make it faster to create internal applications, but teams still need a reliable way to run these apps, connect them to external systems, and fit them into their processes. Datadog Apps gives you a code-first way to build applications from the tools your team already uses, manage governance and observability from Datadog, and embed them directly into Datadog dashboards and workflows.

To learn more, you can view the Datadog Apps documentation and sign up for the Preview. Or, if you’re new to Datadog, you can sign up for a free 14-day trial.