# Datadog | The Monitor blog Check out The Monitor, Datadog's main blog, to learn more about new Datadog products and features, integrations, and more. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## From alert to resolution: Manage incidents with Bits Chat in Slack DevFeed: [From alert to resolution: Manage incidents with Bits Chat in Slack]() Original publisher: [Read original article]() Author: Nancy Zhu; Evan Marcantonio; Nicole Parisi; Chris Miller Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [incident](), [Slack](), [Artificial Intelligence](), [telemetry](), [Code](), [Pull Request]() Tags: [ai](), [also](), [bits-ai](), [code](), [collaboration](), [ecommerce](), [incident](), [monitor](), [root-cause-analysis](), [slack](), [telemetry]() ### AI overview This tutorial explains how Bits Chat in Slack can support incident response by investigating alerts, analyzing telemetry, coordinating responders, generating a pull request for a fix, and tracking follow-up work within the incident channel. ### Source excerpt Use Bits Chat in Slack to investigate incidents, collaborate with responders, make code fixes, and capture follow-up work where your team communicates. ## Transform and route security logs to Microsoft Sentinel tables using Observability Pipelines DevFeed: [Transform and route security logs to Microsoft Sentinel tables using Observability Pipelines]() Original publisher: [Read original article]() Author: Zara Boddula; Danielle Park Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [observability pipelines](), [SIEM, Security](), [Security](), [log management](), [Microsoft]() Tags: [azure](), [cisco-meraki](), [devsecops](), [fortigate](), [log-management](), [logs](), [observability-pipelines](), [pipelines](), [security](), [siem](), [telemetry](), [threat-detection]() ### AI overview Datadog's Observability Pipelines Packs transform firewall, VPN, and network detection logs into Microsoft Sentinel table schemas before ingestion. The post describes Packs for Palo Alto Networks, Fortinet, Cisco ASA, Cisco Meraki, and ExtraHop, including filtering and noise reduction to help control Sentinel ingest volume while retaining visibility. ### Source excerpt Learn how Observability Pipelines Packs map security logs to Microsoft Sentinel schemas and help control downstream ingest volume. ## Manage Cursor costs with Datadog Cloud Cost Management DevFeed: [Manage Cursor costs with Datadog Cloud Cost Management]() Original publisher: [Read original article]() Author: Dom Nguyen; Doug Gunter Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [cloud cost management](), [cursor](), [finops](), [ai-coding]() Tags: [ai](), [ai-coding](), [analysis](), [cloud-cost-management](), [configuration](), [datadog](), [features](), [filter](), [finops](), [monitor](), [monitors](), [product](), [software]() ### AI overview This tutorial explains how Datadog Cloud Cost Management helps teams analyze Cursor spending by user, model, usage mode, and billing group. It covers identifying cost drivers, detecting unexpected changes, correlating spend with usage, and using monitors, budgets, and dashboards to manage AI coding costs alongside cloud and SaaS spending. ### Source excerpt Analyze Cursor spend by user and model, catch unexpected cost changes, and manage AI coding costs alongside your cloud and SaaS spend. ## Monitor TAS and gang scheduling for AI training in Kubernetes DevFeed: [Monitor TAS and gang scheduling for AI training in Kubernetes]() Original publisher: [Read original article]() Author: David Lentz; Kathy Lin Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Kubernetes](), [kueue](), [distributed-training](), [GPU](), [Monitoring](), [datadog]() Tags: [ai](), [batch](), [containers](), [datadog](), [distributed-training](), [gpu](), [gpu-monitoring](), [kubernetes](), [kueue](), [monitoring](), [scheduler]() ### AI overview This article explains why Kubernetes scheduling is insufficient for distributed AI training workloads and how topology-aware scheduling and gang scheduling address hardware placement and simultaneous startup requirements. It discusses implementing these capabilities with Kueue and the Coscheduling plugin, and monitoring and troubleshooting them with Datadog GPU Monitoring. ### Source excerpt Learn how Datadog helps you correlate Kueue, Coscheduling, GPU, and training framework signals to validate gang scheduling and topology-aware scheduling. ## How to operate shared platforms safely at agent scale DevFeed: [How to operate shared platforms safely at agent scale]() Original publisher: [Read original article]() Author: Candace Shamieh; T Zhang; Gabriele Baldoni Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Platform Engineering](), [Loop Engineering](), [AI Agent](), [sdlc](), [Latency](), [Low Latency](), [Concurrency](), [Software]() Tags: [agent](), [agent-observability](), [agentic-workflows](), [agents](), [ai](), [ai-agent](), [ci](), [concurrency](), [latency](), [low-latency](), [operational](), [performance](), [platform-engineering](), [platforms](), [queue](), [recovery](), [timeout]() ### AI overview This Datadog article explains how platform teams can operate shared platforms safely as AI agent workloads scale across teams. It discusses modeling demand across agent trajectories, planning capacity across dependencies such as CI queues and sandbox pools, handling contention and recovery behavior, and preserving control across system boundaries. ### Source excerpt Learn how Datadog models agent demand, allocates capacity under contention, and preserves control as AI agent workloads scale across shared platforms. ## Datadog named the Company to Beat for observability platforms in 2026 Gartner® AI Vendor Race report DevFeed: [Datadog named the Company to Beat for observability platforms in 2026 Gartner® AI Vendor Race report]() Original publisher: [Read original article]() Author: Yanbing Li Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [observability](), [agent observability](), [observability ai agents](), [incident](), [MCP Server](), [observability pipelines](), [OpenTelemetry](), [Security](), [Artificial Intelligence]() Tags: [agent-observability](), [ai-agents](), [datadog](), [incident-response](), [mcp-server](), [observability](), [observability-pipelines](), [opentelemetry]() ### AI overview Datadog says it was named the Company to Beat for observability platforms in Gartner's August 2026 AI Vendor Race research and a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms. The article presents Datadog's unified observability and security platform, including autonomous incident investigation, AI agent and LLM application observability, an MCP Server for querying telemetry, and Observability Pipelines with OpenTelemetry support. ### Source excerpt Datadog has been recognized as the Company to Beat for observability platforms in the August 2026 Gartner® AI Vendor Race research. ## Analyze your experiments in ChatGPT with the Datadog Experiments plugin DevFeed: [Analyze your experiments in ChatGPT with the Datadog Experiments plugin]() Original publisher: [Read original article]() Author: Jonathan Fulton; Amy Zhou; Uday Tennety Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [experiments](), [data](), [Latency]() Tags: [ai](), [analysis](), [chatgpt](), [experimentation](), [experiments](), [openai]() ### AI overview Datadog introduces a ChatGPT Work plugin that lets teams query current experiment results in plain language while retaining Datadog's statistical methods, guardrails, and connected context. ### Source excerpt Learn how the Datadog Experiments OpenAI Data plugin lets your team read, question, and act on experiment results directly in ChatGPT. ## Understanding NetFlow duplication: Why it happens, and how to deduplicate DevFeed: [Understanding NetFlow duplication: Why it happens, and how to deduplicate]() Original publisher: [Read original article]() Author: Julie Wang; Garrison Stauffer Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [telemetry]() Tags: [how-to](), [metrics](), [monitoring](), [netflow](), [network-device-monitoring](), [networks]() ### AI overview The article explains why NetFlow flow records can be duplicated and how duplicate traffic data distorts network analytics. It covers ingress/egress and multi-exporter duplication, recommends selective monitoring, and introduces deduplication in Datadog. ### Source excerpt Learn about the underlying factors that cause duplication of NetFlow traffic flow data, how to prevent the issue, and how Datadog can help. ## How we built Datadog Experiments DevFeed: [How we built Datadog Experiments]() Original publisher: [Read original article]() Author: Chas DeVeas; Aaron Silverman; Tyler Buffington; Jonathan Fulton; Taylor Overturf Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [experiments](), [real user monitoring]() Tags: [a-b-testing](), [acquisition](), [data-analytics](), [data-engineering](), [experimentation](), [experiments](), [metrics](), [observability](), [product-analytics](), [real-user-monitoring]() ### AI overview Datadog describes rebuilding its experimentation platform to speed up confident A/B-test decisions. The article explains a flexible CUPED approach that reduces metric variance and can be applied to segments. ### Source excerpt Datadog Experiments shortens the time from result to decision with CUPED on percentiles, verifiable warehouse results, and near real-time RUM metrics. ## Troubleshoot Kafka issues across every layer of your stack with Kafka Console DevFeed: [Troubleshoot Kafka issues across every layer of your stack with Kafka Console]() Original publisher: [Read original article]() Author: Tori Engler; Shelly Matskel Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Kafka](), [configuration]() Tags: [apache](), [data-streams-monitoring](), [debugging](), [event-streaming](), [infrastructure](), [kafka](), [monitor](), [performance](), [root-cause-analysis]() ### AI overview An overview of Datadog Kafka Console for diagnosing Kafka health and performance issues, inspecting messages, and tuning configurations. ### Source excerpt Learn how Kafka Console helps you identify Kafka issues, inspect messages, and tune configurations with infrastructure and app context. ## How we built data-driven AI Golden Paths at Datadog DevFeed: [How we built data-driven AI Golden Paths at Datadog]() Original publisher: [Read original article]() Author: Addie Beach; Rui Martins Lacerda Published: 2026-09-09T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Bots]() Tags: [agent](), [agent-observability](), [ai](), [cli](), [cost](), [dashboards](), [development](), [documentation](), [experiments](), [performance](), [security](), [skills](), [workflows]() ### AI overview Datadog describes a data-driven process for establishing AI Golden Paths: standardized AI-agent workflows governed by controls such as skills, hooks, and tests. The approach evaluates controls for their effects on code quality, security, token usage, cost, and agent performance. ### Source excerpt See how a Datadog guild achieved 13% faster agent runs by building Golden Paths for AI-assisted development using controls, experiments, and dashboards. ## Coordinate product launches with Datadog DevFeed: [Coordinate product launches with Datadog]() Original publisher: [Read original article]() Author: Milene Darnis; Adam Virani Published: 2026-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [Instrumentation](), [experiments]() Tags: [ai](), [bits-ai](), [digital-experience-monitoring](), [experimentation](), [experiments](), [feature-flags](), [instrumentation](), [launch](), [product-analytics](), [session-replay]() ### AI overview A tutorial on using Datadog Product Analytics Launches to plan product releases, define measurement questions, create tracking plans, and identify missing events and properties before rollout. ### Source excerpt Learn how to turn a product brief and feature flag into a connected launch workflow for instrumentation, experimentation, QA, and reporting. ## Stop runtime threats with Workload Protection response actions DevFeed: [Stop runtime threats with Workload Protection response actions]() Original publisher: [Read original article]() Author: Théo Putegnat; Taylor Overturf Published: 2026-09-04T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Processes](), [Vulnerabilities]() Tags: [datadog-agent](), [processes](), [security](), [threat-detection](), [vulnerabilities](), [workload-protection]() ### AI overview Datadog Workload Protection adds automated and manual runtime response actions. Agent rules can terminate matching malicious processes automatically, while analysts can investigate signals and respond manually. ### Source excerpt When a runtime threat appears, every step costs time. Datadog Workload Protection can now kill processes and isolate workloads with automated and manual response actions. ## Build and run Datadog workflows from Bits Chat or AI agents DevFeed: [Build and run Datadog workflows from Bits Chat or AI agents]() Original publisher: [Read original article]() Author: Neha Haresh; Gabriel Margolis; Brianna Wang; David Robert-Ansart Published: 2026-09-03T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Bots](), [cursor](), [incident]() Tags: [ai](), [ai-agents](), [ai-coding](), [bits-ai](), [claude-code](), [codex](), [cursor](), [debug](), [incident-management](), [mcp](), [workflow-automation](), [workflows]() ### AI overview Datadog describes using Bits Chat and AI coding agents to create, run, and debug automated workflows with Datadog and development context. ### Source excerpt Build, debug, and run Datadog workflows from Bits Chat and AI coding agents using the operational and development context where you're working. ## Monitor prompt caching to optimize your token usage DevFeed: [Monitor prompt caching to optimize your token usage]() Original publisher: [Read original article]() Author: Thomas Sobolik Published: 2026-09-02T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [Caching](), [Large Language Model](), [Latency](), [Traces](), [SIEM, Security, Observability](), [anthropic]() Tags: [agent-observability](), [agents](), [ai](), [ai-engineering](), [ai-observability](), [anthropic](), [caching](), [cost](), [how-to](), [latency](), [llm](), [models](), [monitor](), [openai](), [traces]() ### AI overview A tutorial on prompt caching for LLM and agent workloads, covering cache behavior, provider differences, and monitoring token use and latency. ### Source excerpt Learn how to use prompt caching effectively and monitor your models and agents to troubleshoot cache invalidations. ## From traces to experiments: A loop for improving AI agents DevFeed: [From traces to experiments: A loop for improving AI agents]() Original publisher: [Read original article]() Author: Adam Virani; Lukas Goetz-Weiss; Natasha Silva Published: 2026-09-01T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [ai observability](), [AI Bots]() Tags: [agent-observability](), [agents](), [ai](), [ai-agents](), [ai-observability](), [evaluation](), [experiments](), [latency](), [optimization](), [production](), [telemetry](), [traces]() ### AI overview The article explains how teams can use AI-agent trace data, evaluations, and production experiments to identify performance issues and test whether changes improve outcomes. ### Source excerpt Learn how to read AI agent traces as a roadmap and how to run production experiments that measure whether improvements hold in production. ## Making Rust observability reliable at scale with OpenTelemetry DevFeed: [Making Rust observability reliable at scale with OpenTelemetry]() Original publisher: [Read original article]() Author: Björn Antonsson; Paul Le Grand des Cloizeaux; Scott Gerring Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [tracing](), [telemetry](), [Traces](), [Instrumentation]() Tags: [apm](), [instrumentation](), [latency](), [observability](), [opentelemetry](), [production](), [rust](), [scale](), [traces](), [tracing]() ### AI overview Datadog describes building an opinionated Rust tracer on OpenTelemetry to improve trace propagation, sampling consistency, and trace quality in production services. ### Source excerpt Learn how Datadog improved Rust tracing by building an opinionated OpenTelemetry-based library to help ensure consistent sampling, propagation, and trace quality at scale. ## Visualize how CUPED adjusts experiment results with Datadog DevFeed: [Visualize how CUPED adjusts experiment results with Datadog]() Original publisher: [Read original article]() Author: Tyler Buffington; Lukas Goetz-Weiss; Ryan Lucht Published: 2026-09-01T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [experiments]() Tags: [analysis](), [digital-experience-monitoring](), [experiment](), [experiments](), [how-to](), [metrics](), [product-analytics](), [visualization]() ### AI overview This tutorial explains Datadog Experiments' CUPED adjustments visualization, which breaks the difference between raw and CUPED-adjusted experiment lift into individual covariate contributions. ### Source excerpt Learn how Datadog visualizes CUPED adjustments so you can trace which covariates change experiment lift estimates and improve precision. ## Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration DevFeed: [Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration]() Original publisher: [Read original article]() Author: Eric Metaj; Mark Azer Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Developer Tools](), [iac-security](), [Vulnerabilities]() Tags: [apm](), [bitbucket](), [ci-visibility](), [cloud](), [code](), [code-profiling](), [code-security](), [devsecops](), [error-tracking](), [infrastructure-as-code](), [integration](), [observability](), [open-source](), [pull-requests](), [secret-scanning](), [security](), [software-delivery](), [test-optimization](), [vulnerabilities]() ### AI overview Datadog's Bitbucket Cloud Source Code integration links selected repositories with Datadog so teams can investigate production issues with source context and review security findings before merging code. ### Source excerpt Connect Bitbucket Cloud to Datadog to troubleshoot with source code in context and surface test, quality, and security feedback in pull requests. ## How Bits Database Optimization proves a query rewrite is faster DevFeed: [How Bits Database Optimization proves a query rewrite is faster]() Original publisher: [Read original article]() Author: Alex Weisberger; Nenad Noveljić; Bowen Chen Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Optimization](), [database monitoring](), [Database](), [synthetic-data](), [datasets](), [cpu](), [IO](), [Security](), [Statistics]() Tags: [cpu](), [data](), [database](), [database-monitoring](), [infrastructure](), [optimization](), [performance](), [security](), [software](), [sql](), [statistics](), [synthetic](), [synthetic-data]() ### AI overview The article explains how Bits Database Optimization validates that a proposed query rewrite is faster. It describes controlled benchmarking with simulated production-like datasets, accounting for cache state, CPU and I/O contention, execution time, and database work. ### Source excerpt Learn how Bits generates synthetic data, measures simulation fidelity, and uses execution time and database work to determine whether an optimization is truly faster. ## Respond to security threats faster with Tines and Observability Pipelines DevFeed: [Respond to security threats faster with Tines and Observability Pipelines]() Original publisher: [Read original article]() Author: Zara Boddula Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Security](), [observability](), [Automation](), [log management](), [Orchestration](), [API]() Tags: [accelerate](), [api](), [automation](), [devsecops](), [logs](), [observability-pipelines](), [orchestration](), [real-time](), [reduce](), [security](), [workflows]() ### AI overview Tines and Datadog Observability Pipelines automate security-log processing by standardizing and routing logs, updating pipelines through APIs and reference tables, and applying current context in real time. The integration helps reduce alert noise, identify access-control gaps and suspicious activity, and accelerate threat investigations. ### Source excerpt Learn how Tines workflows can update Datadog Observability Pipelines to prioritize threats, reduce alert noise, and accelerate investigations. ## Reduce sensitive data exposure with build-time allowlists DevFeed: [Reduce sensitive data exposure with build-time allowlists]() Original publisher: [Read original article]() Author: Congyao Zheng; Rick Klein; Seth Fowler Published: 2026-08-28T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [real user monitoring](), [SDKs](), [Web Development]() Tags: [browser](), [digital-experience-monitoring](), [frontend](), [instrumentation](), [javascript](), [privacy](), [real-user-monitoring](), [rust](), [session-replay]() ### AI overview Datadog explains build-time allowlists for RUM action names. A plugin extracts static text from compiled artifacts so the Browser SDK can preserve readable static labels while masking runtime-generated text that may be sensitive. ### Source excerpt Learn how build-time allowlists preserve useful RUM action names while reducing the risk of exposing runtime-generated sensitive data. ## What we learned about AI agent security by monitoring our agents DevFeed: [What we learned about AI agent security by monitoring our agents]() Original publisher: [Read original article]() Author: Alexa Levine; Emmanuelle Lejeail; Mallory Mooney Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Bots]() Tags: [ai-agent](), [ai-security](), [identity](), [logs](), [monitoring](), [security](), [telemetry](), [tools](), [tracing]() ### AI overview Datadog describes security monitoring practices for AI agents, focusing on inventorying components and correlating session-wide telemetry to investigate actions, sensitive-data access, identities, and tool calls. ### Source excerpt Learn what we discovered about AI agent security by monitoring our agents, from creating an inventory of agent components to tracing sensitive data and tool calls. ## Debug live production code without redeploying with Datadog Live Debugger DevFeed: [Debug live production code without redeploying with Datadog Live Debugger]() Original publisher: [Read original article]() Author: Eric Metaj; Sarah Stonehill Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [debugging](), [Instrumentation](), [AI-assisted coding](), [Code]() Tags: [agents](), [ai](), [apm](), [bits-ai](), [code](), [debug](), [debugger](), [debugging](), [developer](), [ide](), [instrumentation](), [production](), [root-cause-analysis](), [tracing]() ### AI overview Datadog Live Debugger helps developers investigate production bugs without changing, restarting, or redeploying application code. It captures runtime details through logpoints, including variable values, method arguments, execution context, and request paths. Bits AI can analyze linked source code, place non-breaking logpoints, interpret collected data, and suggest fixes grounded in production behavior. ### Source excerpt Learn how Live Debugger helps you investigate production code and debug faster using Bits AI. [Next page]()