The developer side of Datadog. Service status: https://xitter.cf/t.co/5hNCi33OVR
Joined March 2026
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LAUNCHED: Skills Library for Datadog Bits AI agents
Turn investigations, notebooks, and team knowledge into reusable skills your agents can use across workflows.
Teach your agents once. Reuse that expertise everywhere.
See it in action 👇
@datadoghq
Dev Tip of the Day: Your AI coding agent can help you debug your AI agents 👀
With Datadog's MCP Server, you can give tools like @claudeai Code, @cursor_ai, @cognition Devin, and @OpenAI Codex access to your Agent Observability data, right from your development environment.
Enable the llmobs toolset to:
→ Search traces and investigate failed agent executions
→ Inspect model calls, tool invocations, and errors
→ Review recurring issues and recommended fixes
→ Analyze experiment results and evaluation metrics
set it up: docs.datadoghq.com/llm_obser…
@ClaudeDevs @OpenAIDevs @datadoghq
Datadog Developers retweeted
If your inference latency or uptime keeps you up at night, you belong at INFER/CON 😅
SF, Nov 16, with some of my favorite people to nerd out with on inference and performance: @dzhulgakov, @charles_irl , and Alexis Lê-Quôc (@datadoghq), plus many more.
Join us!
Datadog Developers retweeted
London sold out. NYC is next.
AI DevCon New York, 2–4 Nov, Brooklyn: 700+ engineers on agents after the demo.
On the agenda:
→ @GoogleDeepMind: stop babysitting your agents
→ @SpaceXAIMemphis : PRs written end to end by agents
→ @datadoghq: is your agent rollout working?
→ @temporalio: when agents run their own tools
Workshop seats are limited: aidevcon.io
Datadog Developers retweeted
Devin Oncall は @datadoghq、@Dynatrace、@sentry などと、設定不要ですぐに連携できます。
オブザーバビリティ・コミュニティの皆さんとパートナーシップを組み、本番環境のインシデントを調査と修正につなげていきます。エンジニアがもっと「つくる」ことに時間を使えるように。
Datadog Developers retweeted
New research from @datadoghq Security Labs! We spent some time poking at Jev to see how we could steer its evaluations.
We found a method that dropped its prompt-injection detection rate from ~83% to ~4% in our tests. We also used the technique to bypass LangChain’s experimental Jev-based tool-risk gating feature. Read more below:
securitylabs.datadoghq.com/a…
Datadog Developers retweeted
"If there is a time engineers can afford to take on technical debt, it's now. A more capable model can repay it down the line. Don't overthink it."
This reframes tech debt in a way that's underappreciated and extremely pragmatic advice for those building with AI today. From a chat with @stevenkplus1 at @datadoghq, with a packed cafe on a Monday afternoon. We covered Devin, AI and math, formal verification, appreciating life, and more.
Reminded me of this slide on tech debt from a talk at Datadog.
xitter.cf/nerdsane/status/210780…
Spotted in TLDR trending - Datadog Scout 1.0 👀
read all about it: a.tldrnewsletter.com/web-ver…
@datadoghq
WEBINAR: From production alert to code fix, without leaving @SlackHQ.
Join @datadoghq and @SlackHQ on Oct 28 to see how Code Channels connects Datadog observability with collaborative development.
Save your spot 👉 slack.com/events/from-build-…
Datadog Developers retweeted
hop in, we're fixing prod
Exciting news from Datadog AI Research: we trained a model to investigate production incidents, and it matches the best frontier models at a fraction of the cost.
Introducing Scout 1.0, the model powering Bits Investigation, our AI SRE.
On AlertEval, our benchmark of real production alerts:
- Scout 1.0 medium matches Claude Opus 5.5 high and GPT-6.1 Sol high at 3-8x lower cost
- It also performs comparably to Claude Fable 5.1 medium and GPT-6 Astra medium at 12x and 19x lower cost
- Scout 1.0 xhigh matches Claude Fable 5.1 xhigh at 4x lower cost
Datadog Developers retweeted
🎙️ We're joined by Yanbing Li, Chief Product Officer at @datadoghq, to talk about applying observability to non-deterministic AI agents, blurring the boundaries between software development and production workflows, and navigating emerging challenges in AI security and tokenomics.
stackoverflow.blog/2026/10/0…
Another week, another round up - Shipped this week at @datadoghq 🚀
- Running a frontier model on every alert gets expensive fast. We launched Scout 1.0, our own model built specifically for incident investigation: matches frontier-model quality on real production alerts at 3 to 19x lower cost, and already powers Bits Investigation today. (@ThisIsOthmane)
- Agents built on @claudeai used to have limited production visibility. Claude Platform is now live with native OTel trace export, with Datadog as a launch partner. @claudeai @AnthropicAI
- Building test coverage meant scripting it and maintaining it by hand. Bits Testing is live now: it discovers critical user flows and generates tests for them, one click to adopt.
- Vulnerabilities that span several steps used to slip past pattern-matching scanners. AI-native SAST is live now, tracing how data actually moves through your code.
- Incident details live in Slack and bridge calls, not just telemetry? Bits AI Investigations already ingest the whole incident channel, not just monitor alerts.
- Signals explaining user drop-off used to be scattered across three products. Journey Monitoring is live now, unifying synthetic coverage, RUM, and product analytics in one view.
- A network issue meant context-switching across tools to find the blast radius? NDM now correlates issues to endpoints, network paths, and VPN tunnels on one screen.
Full write-up + docs → github.com/DataDog/shipped/d…
Your agents are in production. Now see everything they do.
Datadog Agent Observability is now available on the @claudeai Marketplace. Trace every step of your Claude-powered agents, correlated with the rest of your stack.
Learn more: bit.ly/3WtGX6Y
Datadog Developers retweeted
Two years ago, when we started building our SRE agent, we bet heavily on evals. We hand-labelled thousands of production incidents and built the infrastructure needed for our engineers to run this golden dataset every day as they iterated on the agent. That work underpins how we evaluate Scout 1.0. Specialized models start with knowing what good looks like in your domain.
Meet Scout 1.0, our first AI model built for investigating production incidents.
Scout matches frontier-model investigation quality at a fraction of the cost, which makes it practical to investigate more issues, earlier.
We trained Scout on realistic simulations built from real-world incidents and production telemetry, using reinforcement learning to reward accurate root causes backed by evidence.
Learn more and sign up for the preview: bit.ly/3TJknGO
Datadog Developers retweeted
Dead simple to connect your Claude Platform to @datadogdevs
Check it out:
LAUNCHED: @claudeai now supports native OpenTelemetry trace export to @datadoghq Agent Observability.
Trace model calls, pinpoint failures, and inspect token usage and costs. No custom exporter required.
See how we debugged a failed flight booking in this demo 👇
LAUNCHED: @claudeai now supports native OpenTelemetry trace export to @datadoghq Agent Observability.
Trace model calls, pinpoint failures, and inspect token usage and costs. No custom exporter required.
See how we debugged a failed flight booking in this demo 👇
docs:
@datadoghq - docs.datadoghq.com/llm_obser…
@AnthropicAI - platform.claude.com/docs/en/…
Anthropic's announcement - claude.com/resources/article…
Introducing Scout 1.0 - our first AI model built for investigating production incidents
try it out: datadoghq.com/blog/ai/introd…
Meet Scout 1.0, our first AI model built for investigating production incidents.
Scout matches frontier-model investigation quality at a fraction of the cost, which makes it practical to investigate more issues, earlier.
We trained Scout on realistic simulations built from real-world incidents and production telemetry, using reinforcement learning to reward accurate root causes backed by evidence.
Learn more and sign up for the preview: bit.ly/3TJknGO