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What problem is agentic AI trying to solve?
Agentic AI isn’t limited to security operations. It’s already improving hospitals, financial systems, and service industries by reducing overload and filling skill gaps. Here’s the problem it was actually built to solve. Additional Resources: About Elastic Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale. Elastic’s solutions for search, observability, and security are built on the Elastic Search AI Platform — the development platform used by thousands of companies, including more than 50% of the Fortune 500.
The Problem With AI Code Nobody's Talking About
GitKraken's Product Director Justin Roberts explains why we're focused on managing AI-generated code aftermath, not creating another code generator. Learn about Commit Composer, AI conflict resolution, GitKraken MCP, and our agent-driven workflow vision.
What is agentic AI? (explained in 60 seconds)
Agentic AI is the next evolution of artificial intelligence. Unlike traditional AI, it can act autonomously and make decisions on its own. Here’s what that actually means, without the hype. Additional Resources: About Elastic Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale. Elastic’s solutions for search, observability, and security are built on the Elastic Search AI Platform — the development platform used by thousands of companies, including more than 50% of the Fortune 500.
AI SRE in Practice: Tracing Policy Changes to Widespread Pod Failures
Policy changes in Kubernetes are supposed to improve security, enforce standards, or optimize resource usage. But when a policy change triggers cascading pod failures across multiple namespaces, the investigation becomes a race to identify what changed before more workloads are affected.
The rise of the agentic future: scaling AI workflows with relaxAI and n8n
This blog is based on the webinar, “From idea to agent: Building AI workflows with relaxAI and n8n”. You can watch the full recording by clicking here! AI isn’t slowing down. We’re moving from “ask a chatbot” to agents that run the multi-step workflows, use tools, and are built for real business processes. Most teams aren’t blocked by ideas. They’re blocked by three things: complexity, cost, and control.
AI Vendor Lock-In: How AI Is Creating A New Dependency Problem
Like most SaaS companies, you’re under pressure to ship AI-powered features faster, smarter, and at scale. For many teams, that pressure leads to relying on external AI platforms, managed models, and third-party APIs instead of building everything from scratch in-house. At first, it feels like a win. Your team ships an AI-powered feature in weeks instead of months. No GPU clusters to manage. No models to train. No infrastructure to babysit.
Beyond boundaries: How global collaboration defines AI in 2026
As we move through 2026, the global conversation around AI is shifting from simple adoption to a deeper focus on true openness and sovereignty. In this session from Civo Navigate India 2025, OpenUK CEO Amanda Brock explores the evolving state of AI openness and shares a significant milestone: India is now the world’s number one open-source contributing community.
Agentic AI in DevOps: The Architect's Guide to Autonomous Infrastructure | Harness Blog
For the last decade, the holy grail of DevOps has been Automation. We spent years writing Bash scripts to move files, Terraform to provision servers, and Ansible to configure them. And for a while, it felt like magic. But any seasoned engineer knows the dirty secret of automation: it is brittle. Automation is deterministic. It only does exactly what you tell it to do. It has no brain. It cannot reason.
AI NetOps: How AI and Machine Learning Transform Network Operations
AI is changing network operations (NetOps) from static automation into adaptive, data-driven systems that can summarize incidents, retrieve knowledge, and guide remediation with human oversight. In this talk, Phil Gervasi breaks down what “AI for NetOps” really means in practice, including the difference between classical ML and large language models (LLMs), why data pipelines matter more than model tuning, and how patterns like RAG (retrieval augmented generation), text-to-SQL, and agentic workflows turn raw telemetry into decisions.