RapDev announced the latest extension of Arlo, its suite of AI Agents, for Datadog environments - built to streamline observability workflows, reduce operational toil, and accelerate incident resolution.
Launching late Q2 on the Datadog Marketplace, Arlo leverages RapDev's deep engineering expertise to deliver proactive AI-driven solutions that automate investigation and troubleshooting of incidents in real-time, using several LLM techniques. Arlo empowers SREs and engineering teams to stay focused on innovation, avoiding manual troubleshooting and infrastructure noise.
By embedding prompt-chaining techniques directly into Datadog environments, Arlo delivers real-time diagnostics, actionable remediation, and autonomous response capabilities, making incident resolution both faster and smarter.
Each Arlo Agent targets a specific area of infrastructure or application health, offering measurable outcomes and fast time-to-value:
- Arlo for Linux: Flags disk space issues, identifies large or runaway log files, and initiates cleanup actions before business services are impacted.
- Arlo for Kubernetes: Surfaces saturation and deployment anomalies at the node level, with built-in recommendations to reduce drift and prevent future failure.
- Arlo for Windows: Identifies memory pressure and system constraints on Windows VMs hosting .NET applications, pinpointing exactly which processes to address.
- Arlo for Networking: Diagnoses spanning tree and switch-level issues by logging into network devices and identifying misconfigurations, cutting network troubleshooting time dramatically.
"Arlo takes the burden off engineers by running real troubleshooting workflows across Linux, Windows, Kubernetes, and network devices," said Jay Barker, Director of Datadog Engineering at RapDev. "Whether it's root cause analysis or live remediation, Arlo accelerates incident response and turns SRE hours into minutes - all within your existing Datadog environment."
Whether investigating root causes, recommending fixes, or running commands directly, Arlo acts as a digital teammate that never sleeps - automating repetitive diagnostics so teams can resolve issues with confidence and speed.
"Arlo is built for the next wave of ProdOps," said Tameem Hourani, Principal and Founder at RapDev. "With agents that act directly within your observability workflows, it's not just surfacing data - it's taking action. That's where our industry is headed: AI agents that troubleshoot, resolve, and never sleep, so your teams can."
Arlo for Datadog exemplifies RapDev's commitment to building AI-native solutions that enhance core platform capabilities, improve engineer productivity, and deliver operational efficiencies at scale.
Arlo's launch marks a significant milestone in RapDev's AI strategy, with additional agent capabilities and customer-driven enhancements already in development.
The Latest
For all the attention AI receives in corporate slide decks and strategic roadmaps, many businesses are struggling to translate that ambition into something that holds up at scale. At least, that's the picture that emerged from a recent Forrester study commissioned by Tines ...
From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...
Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...
Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...
Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...
Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...
The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...
In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...
In MEAN TIME TO INSIGHT Episode 19, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA explains the cause of the AWS outage in October ...
The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...