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.
Three vendors – Cohesity, ServiceNow, and Datadog – have partnered to create a recoverability service designed to address the risks associated with agentic AI (AIOps). The service aims to restore systems to a "trusted state" by identifying and recovering files and data corrupted by AI errors or malicious attacks.
The companies anticipate increased adoption of agentic AI for system operation but recognize the potential for errors and vulnerabilities. Their solution focuses on preserving immutable snapshots of AI environments, enabling point-in-time recovery of agents, data, and infrastructure components, including vector stores and agent memory.
ServiceNow and Datadog provide control and observability platforms to detect anomalies, triggering API-driven restorations when problems are identified. This offering competes with Rubrik's similar tool and native rollback capabilities from vendors like Cisco. Gartner predicts a significant increase in the integration of task-specific agents in enterprise applications, while Forrester emphasizes the need for guardrails and strong oversight in agentic AI development.
The companies anticipate increased adoption of agentic AI for system operation but recognize the potential for errors and vulnerabilities. Their solution focuses on preserving immutable snapshots of AI environments, enabling point-in-time recovery of agents, data, and infrastructure components, including vector stores and agent memory.
ServiceNow and Datadog provide control and observability platforms to detect anomalies, triggering API-driven restorations when problems are identified. This offering competes with Rubrik's similar tool and native rollback capabilities from vendors like Cisco. Gartner predicts a significant increase in the integration of task-specific agents in enterprise applications, while Forrester emphasizes the need for guardrails and strong oversight in agentic AI development.
An account of how a developer, Alexey Grigorev, accidentally deleted 2.5 years of data from his AI Shipping Labs and DataTalks.Club websites using Claude Code and Terraform. Grigorev intended to migrate his website to AWS, but a missing state file and subsequent actions by Claude Code led to a complete wipe of the production setup, including the database and snapshots. The data was ultimately restored with help from Amazon Business support. The article highlights the importance of backups, careful permissions management, and manual review of potentially destructive actions performed by AI agents.
This tutorial explores how to use LLM embeddings as features in time series forecasting models. It covers generating embeddings from time series descriptions, preparing data, and evaluating the performance of models with and without LLM embeddings.
This article details how Google SREs are leveraging Gemini 3 and Gemini CLI to accelerate incident response, root cause analysis, and postmortem creation, ultimately reducing Mean Time To Mitigation (MTTM) and improving system reliability.
>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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* **GitHub MCP Server:** Enables interaction with repositories, issues, pull requests, and CI/CD via GitHub Actions.
* **Notion MCP Server:** Allows AI access to notes and documentation within Notion workspaces.
* **Atlassian Remote MCP Server:** Connects AI tools with Jira and Confluence for project management and collaboration. (Currently in beta)
* **Argo CD MCP Server:** Facilitates interaction with Argo CD for GitOps workflows.
* **Grafana MCP Server:** Provides access to observability data from Grafana dashboards.
* **Terraform MCP Server:** Enables AI-driven Terraform configuration generation and management. (Local use only currently)
* **GitLab MCP Server:** Allows AI to gather project information and perform operations within GitLab. (Currently in beta, Premium/Ultimate customers only)
* **Snyk MCP Server:** Integrates security scanning into AI-assisted DevOps workflows.
* **AWS MCP Servers:** A range of servers for interacting with various AWS services.
* **Pulumi MCP Server:** Enables AI interaction with Pulumi organizations and infrastructure.
Logward is an open-source log collector and viewer designed for small environments like home labs. It offers a modern interface and supports Sigma rules for log detection and alerting.
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