
Datadog announced the general availability of Workflow Automation.
The new product enables teams to automate end-to-end remediation processes—with out-of-the-box actions and pre-built templates—across all systems, apps and services to help identify, investigate and resolve service disruptions and security threats faster.
Workflow Automation combines alerts and remediation in a single, streamlined solution. Datadog provides developers and security engineers end-to-end visibility across the tech stack so that teams are alerted to disruptions as they arise. Now, with Workflow Automation, teams can use context-rich alerts to automate entire remediation processes across their tools and services in response to disruptions directly in Datadog's unified platform. By giving teams the ability to both observe the entire tech stack in a single platform and also remediate any issues directly in the same place, Workflow Automation helps organizations maintain the availability of their systems.
"Manual processes and context switching are often the root cause of long stretches of IT downtime. This impacts both a company's bottom line and its reputation with end users," said Michael Gerstenhaber, VP of Product at Datadog. "Workflow Automation provides the automation and context that DevOps, SRE and security teams need in order to remediate issues quickly. Automation that seamlessly connects observability information with remediation allows engineers to proactively respond to insights before they turn into issues that would otherwise affect their businesses."
Workflow Automation enables organizations to:
- Trigger Responses Instantly: Users can automatically trigger responses from observability alerts, security signals and dashboards. These response workflows can be enriched with real-time observability data, such as logs and metrics, that guide automated decision-making. This allows teams to identify, investigate and resolve service disruptions and security threats quickly while proactively maintaining the health of systems.
- Automate Complex Processes: Over 300 out-of-the-box actions and more than 40 pre-built templates enable users to easily automate routine response tasks and complex end-to-end processes to save engineers time, eliminate human error and reduce overhead.
- Safeguard Automation: Teams can create interactive, human-in-the-loop workflows, and safeguard these workflows with granular role-based access control (RBAC) while keeping track of executions with detailed workflow auditing.
The Latest
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...
Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...
Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...
As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...
For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...
I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...
Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...