Operations | Monitoring | ITSM | DevOps | Cloud

Responsible AI Writing: How Teams Use AI Tools Without Losing Authenticity

AI writing tools have made content creation significantly faster. Drafts that once required hours can now be produced in minutes, helping teams scale documentation, communication, and content production. However, speed alone does not guarantee quality. As AI-generated content becomes more common, many teams are finding that raw output often lacks clarity, consistency, or the tone required for professional use.

What Native Audio in AI Video Actually Means for the Future of Content

In 2026, the arrival of native audio has officially ended the silent film era of generative AI. For years, creators had to hunt for sound effects and manually align voiceovers in post-production, but the new standard is simultaneous generation. Native audio means the AI no longer simply adds sound to a finished clip. Instead, models like Seedance 2.0 on the Higgsfield platform generate audio and video together in a single mathematical pass. This shift from fragmented tools to a unified multimodal architecture is fundamentally changing how content is produced.

Why Autonomous AI Agents Can't Run on SaaS Infrastructure

The era of the “copilot” is ending. We are moving rapidly toward the era of the autonomous software factory, where autonomous agents don’t just autocomplete our code—they investigate, plan, test, and merge entire features while we sleep. But this shift has exposed a critical flaw in how we consume AI. For the past decade, the default motion for enterprise software has been SaaS. It’s easy, frictionless, and managed by someone else.

LLM Cost Monitoring with OpenTelemetry

Teams running LLM applications in production face a cost problem that traditional APM tools were never designed to solve. CPU and memory costs are relatively predictable — a web service processing 1,000 requests per second costs roughly the same week over week. LLM API costs are not. A single user session can cost $0.01 or $5 depending on prompt length, model choice, conversation history, and how many retries happen inside your chain.

How AI Is Powering the Next Era of IT Operations

AI is redefining the future of IT. In this Nexus Live 2025 keynote, ScienceLogic CEO and Founder Dave Link shares the vision behind Skylar AI, why the industry is shifting toward autonomous operations, and how organizations can move faster, smarter, and more proactively than ever before. In this session you’ll see.

Deterministic by Design: How Harness Grounds AI Agents in Structured Data | Harness Blog

When AI agents operate across a multi-module platform like Harness (from CI/CD to DevSecOps to FinOps), the number one goal is to give you answers that are correct, consistent, and grounded in real data. Getting there requires a deliberate architectural choice: when a question can be answered from structured platform data, the agent should use a schema-driven Knowledge Graph rather than raw API calls via MCP. The principle is simple: if the data is modeled, retrieval should be deterministic.

Kosli and Adaptavist Partner to Automate Governance for AI driven Software Delivery

Today, Kosli and Adaptavist announce a strategic partnership to help regulated enterprises automate governance for AI driven software delivery - making it automated, continuous, and evidence-driven rather than a manual checkpoint that sits apart from DevOps and CI/CD. Adaptavist brings deep enterprise DevOps transformation expertise: assessment and strategy, DevSecOps integration, developer experience, and implementation across Atlassian, GitLab, and AWS.

AI agent observability: The developer's guide to agent monitoring

Most "agent observability best practices" content reads like a compliance checklist from 2019 with "AI" pasted over "microservices." Implement comprehensive logging. Establish evaluation metrics. Create governance frameworks. Not a single line of code. No mention of what happens when your agent silently picks the wrong tool on turn 3 and you need to figure out why.

Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA

This guest blog post is by Tohn Furutani, SRE Engineer at NTT DATA. Over the past year, the conversation around generative AI has shifted from single-shot use cases—such as summarization, Q&A, and chat interfaces—to agentic AI systems that can make decisions based on context, plan multistep actions, invoke tools, and adapt as conditions change.