This article details how HPE is addressing operational fatigue and burnout in IT teams through the introduction of agentic AI operations. HPE's new system utilizes skills-based AI agents that work alongside human operators to reduce alert noise, improve response times, and cut root cause analysis time by at least half, according to early adopters.
The focus is on augmenting human capabilities rather than replacing them, with a strong emphasis on auditability, transparency, and human oversight in AI-driven actions. The system aims to break down data silos and provide proactive insights to prevent issues before they escalate.
The focus is on augmenting human capabilities rather than replacing them, with a strong emphasis on auditability, transparency, and human oversight in AI-driven actions. The system aims to break down data silos and provide proactive insights to prevent issues before they escalate.
This article discusses how AI is changing infrastructure as code (IaC) and the challenges it presents. Spacelift's co-founder, Marcin Wyszynski, explains that while AI tools can democratize infrastructure provisioning, the lack of understanding of the generated code poses risks. He draws a parallel to learning a foreign language – AI can produce the code, but teams need to comprehend it to avoid potentially disastrous infrastructure changes.
Spacelift's solution, Intent, focuses on deterministic guardrails and integration with tools like Open Policy Agent to ensure safe and controlled AI-driven infrastructure management. The core challenge is balancing speed and control in a rapidly evolving landscape.
Spacelift's solution, Intent, focuses on deterministic guardrails and integration with tools like Open Policy Agent to ensure safe and controlled AI-driven infrastructure management. The core challenge is balancing speed and control in a rapidly evolving landscape.
The Model Context Protocol (MCP) is becoming a key component in the agentic AI space, enabling models to interact with external tools and data. The project's 2026 roadmap focuses on addressing challenges for production deployment. Key priorities include improving scalability by evolving the transport and session model, clarifying agent communication and task lifecycle management, maturing governance structures for wider community contribution, and preparing for enterprise requirements like audit trails and authentication. The roadmap also highlights ongoing exploration of areas like event-driven updates and security.
The New Stack encourages its readers to contribute to Towards Data Science, a leading platform for data science and AI. Recognizing the increasing convergence of cloud infrastructure, DevOps, and AI engineering, the article invites practitioners to share their experiences with building and deploying AI systems. Successful TDS submissions are technically detailed, timely, and specific. Authors can also benefit from editorial support, promotion, and potential payment opportunities, while building their reputation within the AI community.
Agentic workflows are rapidly accelerating the volume of pull requests, and validation is quickly becoming the most critical bottleneck. Teams using service meshes like Istio are well-positioned to solve it in ephemeral environments.
Amazon Web Services (AWS) recently made a significant move by laying off approximately 40% of its DevOps staff. This decision wasn't a sign of downsizing, but rather a strategic shift towards automation and a new tool called 'Dahlia'. This article explores the reasons behind the layoffs, the capabilities of Dahlia, and its potential impact on the future of DevOps.
The article details Amazon Web Services' (AWS) recent decision to lay off a significant portion (around 40%) of its DevOps workforce, specifically those involved in managing and maintaining its own internal infrastructure. This isn't a sign of AWS abandoning DevOps, but rather a strategic shift *towards* fully embracing a "platform engineering" approach and leveraging automation tools.
* **Shift to Platform Engineering:** AWS is building internal "developer platforms" – self-service tools and standardized components – to empower application development teams to manage their own infrastructure and deployments with less reliance on centralized DevOps teams.
* **Key Tools Driving the Change:** The article highlights three main tools enabling this transition:
* **Pulumi:** An Infrastructure-as-Code (IaC) tool allowing developers to define infrastructure using familiar programming languages (Python, JavaScript, Go, etc.).
* **Crossplane:** An open-source Kubernetes add-on that extends Kubernetes to manage infrastructure across multiple cloud providers.
* **Backstage:** A developer portal created by Spotify, now open-source, that provides a centralized interface for developers to discover, create, and manage software components and infrastructure.
* **Impact of the Layoffs:** The layoffs were concentrated in teams traditionally responsible for manual infrastructure provisioning and maintenance. The remaining DevOps staff are being re-focused on building and maintaining the internal developer platforms.
* **Wider Industry Trend:** This move by AWS reflects a broader trend in the industry towards platform engineering, driven by the need for faster innovation, increased developer productivity, and reduced operational overhead.
In essence, AWS is automating away much of the traditional DevOps work, allowing developers to self-serve their infrastructure needs through these platform tools. This is a strategic move to scale its internal development efforts and accelerate innovation.
The article details Amazon Web Services' (AWS) recent decision to lay off a significant portion (around 40%) of its DevOps workforce, specifically those involved in managing and maintaining its own internal infrastructure. This isn't a sign of AWS abandoning DevOps, but rather a strategic shift *towards* fully embracing a "platform engineering" approach and leveraging automation tools.
* **Shift to Platform Engineering:** AWS is building internal "developer platforms" – self-service tools and standardized components – to empower application development teams to manage their own infrastructure and deployments with less reliance on centralized DevOps teams.
* **Key Tools Driving the Change:** The article highlights three main tools enabling this transition:
* **Pulumi:** An Infrastructure-as-Code (IaC) tool allowing developers to define infrastructure using familiar programming languages (Python, JavaScript, Go, etc.).
* **Crossplane:** An open-source Kubernetes add-on that extends Kubernetes to manage infrastructure across multiple cloud providers.
* **Backstage:** A developer portal created by Spotify, now open-source, that provides a centralized interface for developers to discover, create, and manage software components and infrastructure.
* **Impact of the Layoffs:** The layoffs were concentrated in teams traditionally responsible for manual infrastructure provisioning and maintenance. The remaining DevOps staff are being re-focused on building and maintaining the internal developer platforms.
* **Wider Industry Trend:** This move by AWS reflects a broader trend in the industry towards platform engineering, driven by the need for faster innovation, increased developer productivity, and reduced operational overhead.
In essence, AWS is automating away much of the traditional DevOps work, allowing developers to self-serve their infrastructure needs through these platform tools. This is a strategic move to scale its internal development efforts and accelerate innovation.
A recent article by Google Cloud SREs describes how they use the AI-powered Gemini CLI internally to resolve real-world outages. This approach improves reliability in critical infrastructure operations and reduces incident response time by integrating intelligent reasoning directly into the terminal-based operational tools.
This article explores the emerging category of AI-powered operations agents, comparing AI DevOps engineers and AI SRE agents, how cloud providers are responding, and what engineers should consider when evaluating these tools.
Late last year, startup Platform Engineering Labs made waves in the world of Infrastructure as Code (IaC) by introducing a new IaC platform, called Formae, available initially on Amazon Web Services. This week, Platform Engineering Labs‘ platform gets (beta) support from additional cloud platforms, including Google Cloud Platform, Microsoft Azure, Oracle Cloud Infrastructure, and OVHcloud. The company has also released new AI-enhanced software for managing infrastructure tooling, called the Platform for Infrastructure Builders.
>When deployed strategically, agents can empower SREs to offload low-risk, toilsome tasks so they can focus on the most critical matters.
Agents in practice include:
* **Contextual Information:** Providing SREs with details from previously resolved incidents involving the same service, including responder notes.
* **Root Cause Analysis:** Suggesting potential origins of an issue and identifying recent configuration changes that might be responsible.
* **Automated Remediation:** Handling low-risk, well-defined issues without human intervention, with SRE review of after-action reports.
* **Diagnostic Suggestions:** Nudging SREs towards running specific diagnostics for partially understood incidents and supplying them automatically.
* **Runbook Generation:** Automatically creating and updating runbooks based on successful remediation steps, preventing recurring issues.
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Agents in practice include:
* **Contextual Information:** Providing SREs with details from previously resolved incidents involving the same service, including responder notes.
* **Root Cause Analysis:** Suggesting potential origins of an issue and identifying recent configuration changes that might be responsible.
* **Automated Remediation:** Handling low-risk, well-defined issues without human intervention, with SRE review of after-action reports.
* **Diagnostic Suggestions:** Nudging SREs towards running specific diagnostics for partially understood incidents and supplying them automatically.
* **Runbook Generation:** Automatically creating and updating runbooks based on successful remediation steps, preventing recurring issues.
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