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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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Give your AI agents live Datadog access from the command line
Cody Lee, SeGe Jung, Sumedha Mehta, Eddie Cai · 2026-06-05 · via Datadog | The Monitor blog

AI agents are becoming a standard part of how engineers write, deploy, and troubleshoot software. Getting observability data into those workflows, securely and without manual intervention, remains the harder problem.

Pup CLI gives shell-style agents, automation pipelines, and scripting workflows secure, OAuth-scoped access to 33+ Datadog product domains through a single binary with 200+ commands. Agents can discover commands dynamically, reason over structured JSON or YAML output, and use built-in skills and runbooks without managing long-lived API keys. Because agents pull only the commands they need via pup agent schema rather than loading a full tool definition library upfront, working context is more token efficient. The CLI pairs with the Datadog MCP Server, which covers conversational, chat-style workflows in IDEs and assistants.

In this post, you’ll learn how to:

- Give shell-style agents a CLI built for Datadog

- Access the Datadog platform from one binary

- Authenticate once with OAuth

- Add Datadog skills and runbooks to your agent

- Connect any AI client to Datadog locally

Give shell-style agents a CLI built for Datadog

AI agents interact with systems differently depending on where they run. Chat-style assistants inside IDEs need curated, conversational interfaces with confirmation gates and constrained actions. Terminal-native agents, CI/CD workflows, and automation harnesses need composable command-line access that fits naturally into shell workflows.

Modern coding agents are already fluent in shell environments. Models trained on large volumes of terminal interactions understand workflows built around commands such as git, kubectl, jq, and Unix pipes. A well-designed CLI extends an agent’s operational capabilities without additional configuration. Agents can invoke commands, chain outputs, and interpret structured responses using patterns they already know.

Pup CLI builds on that model with a single, consistent Rust binary across Claude Code sessions, Cursor workflows, CI runners, runbooks, and custom agent harnesses. Regardless of where an agent runs, it interacts with the same Datadog backend through the same interface and authentication flow.

Diagram showing Datadog MCP Server for chat-style agents and Pup CLI for shell-style agents, CI pipelines, and automation workflows.

Access the Datadog platform from one binary

Pup CLI gives agents a single interface for the Datadog platform, from metrics and logs to Incident Management, Real User Monitoring, Application Performance Monitoring, Cloud SIEM, Cloud Cost Management, and more without switching tools or managing separate integrations.

The CLI is designed to be discoverable for both humans and agents. Engineers can use standard help commands to explore the available functionality, while agents can retrieve the complete command schema dynamically in machine-readable form.

pup --help

pup agent schema

The pup agent schema command returns the complete command tree as structured JSON, allowing agents to inspect available operations only when they need them. Dynamic schema retrieval reduces the amount of tool metadata that needs to remain in an agent’s working context, leaving more room for reasoning and planning.

Pup CLI returns structured JSON or YAML output by default, making it easy for agents to parse and chain results into additional workflows. Commands integrate naturally with standard shell tooling such as jq, grep, or custom automation scripts.

For example, an agent investigating a production issue could search logs for recent checkout service failures:

pup logs search --query="status:error service:checkout" --from="1h" \

| jq '.data[].attributes.message'

An agent could then pivot into monitor investigations:

pup monitors search --tags="team:api-platform"

pup monitors get <id>

Or retrieve active incidents directly from Datadog:

pup incidents list --status=active \

| jq '.data[].attributes.title'

Beyond core observability workflows, Pup CLI also covers a broad set of the Datadog API surface. Niche admin operations, preview surfaces, and CRUD support for platform configuration objects are reachable through the same binary and authentication layer, without additional setup.

Terminal output showing pup monitors get returning structured JSON for a checkout error rate monitor, including state, tags, and alert thresholds.
Terminal output showing pup monitors get returning structured JSON for a checkout error rate monitor, including state, tags, and alert thresholds.

Authenticate once with OAuth

Many teams rely on long-lived API and application keys spread across shell environments, CI pipelines, Terraform configurations, and internal documentation. Pup CLI addresses this with an OAuth 2.0 + PKCE authentication flow.

Instead of distributing static API keys across multiple environments, you authenticate once through:

Tokens are scoped, revocable, and aware of multi-organization environments. If a device is lost or access needs to be revoked, you can invalidate the associated credentials without rotating every script or automation workflow tied to a static key.

Pup CLI inherits Datadog role-based access control. Agents can only view or act on the resources their authenticated user is already authorized to access. This helps you preserve existing access boundaries while extending Datadog into AI-assisted workflows, including for teams with HIPAA requirements.

A single authenticated session can support multiple local agent workflows, including Claude Code, Cursor, OpenCode, custom agents, and ad hoc scripts. You no longer need to provision and manage separate API keys for every tool or environment.

Managing API keys across CI pipelines, shell environments, and agent tools adds friction when you adopt AI-assisted operations. Pup CLI helps reduce that friction by consolidating access through a single OAuth-scoped interface, using a centralized audit trail and your existing Datadog permissions.

Terminal output showing pup auth login completing an OAuth2 flow with the token stored in macOS Keychain.
Terminal output showing pup auth login completing an OAuth2 flow with the token stored in macOS Keychain.

Add Datadog skills and runbooks to your agent

Pup CLI ships with built-in skills and subagents that give agents reusable operational workflows without manual prompt crafting.

For example, you can install bundled skills into Claude Code:

pup skills install claude-code

This command installs skills into .claude/skills/ and native subagents into .claude/agents/.

Cursor workflows are similarly supported:

pup skills install cursor

You can also browse and install individual skills:

pup skills list

pup skills install claude --name=dd-monitors

Pup CLI integrates directly with the Claude Code plugin marketplace as well:

/plugin marketplace add datadog/pup

The bundled skills support operational workflows such as incident triage, log, metric, and trace correlation, change tracking, and Database Monitoring analysis. If you’re using Claude Code, you can invoke a workflow such as /sre-investigate and receive a guided investigation that moves through logs, traces, deployments, and database telemetry while the agent calls Pup CLI commands behind the scenes. You can use runbook outputs directly in incident reviews and postmortems.

Pup CLI also supports CI/CD automation scenarios such as deploy event creation, source map uploads, DORA metrics collection, and SLO validation. Each of these workflows uses the same OAuth-scoped authentication as local sessions, so no additional secrets need to be provisioned or rotated in CI configuration.

A Claude Code session where a user prompts an investigation which calls Pup to query logs and monitor data.

Connect any AI client to Datadog locally

Some organizations use proprietary AI agents or clients that do not integrate directly with Datadog. Pup CLI connects those environments to Datadog through the pup acp serve command, which runs a local, OAuth-scoped AI server.

Pup CLI supports two protocols out of the box:

  • ACP (Agent Communication Protocol) for ACP-native clients

  • OpenAI-compatible /chat/completions endpoints for OpenCode, Cursor, and any @ai-sdk/openai-compatible client

By default, Pup CLI automatically discovers your first Datadog AI agent. You can also target a specific agent instance:

pup acp serve --agent-id <uuid> --port 9099

Once running, any compatible client can connect locally:

Running Pup CLI as a local server lets you connect internal or experimental AI tooling to live Datadog telemetry data without waiting for vendor-specific integrations. Datadog remains the operational source of truth while you retain flexibility in how you build and deploy AI workflows.

Diagram showing AI coding tools connecting to Pup locally, with Pup routing requests to Datadog AI agents and observability data.

Bring Datadog into your AI agent workflows

AI agents are becoming a standard part of software delivery and operations, but most still lack direct access to live production telemetry. Pup CLI gives shell-style agents a single command-line interface for interacting with the Datadog platform across debugging sessions, automation workflows, CI/CD pipelines, and custom agent ecosystems. It gives your agents OAuth-scoped access to live Datadog telemetry, with built-in skills for agent workflows and no long-lived API keys required. The result is a consistent way to bring Datadog telemetry into your agent workflows.

If you’re new to Datadog, you can sign up for a 14-day free trial to start giving your AI agents access to live Datadog telemetry data.