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RapDev Launches Arlo

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.

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RapDev Launches Arlo

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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 ...