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Discover how ignio’s AI-powered agents are transforming IT event and alert management by combining Agentic AI, AI/ML algorithms and automation. In this video, we introduce ignio AI Agent for IT Event Management — a purpose-built, autonomous agent designed to reduce alert noise, group related alerts and predict future events. Whether you’re managing a large-scale enterprise infrastructure, cloud-native environment, or hybrid IT setup, this AI agent empowers your SRE and IT operations (ITOps) teams with real-time observability, automated alert correlation and suppresion, and predictive intelligence What You’ll Learn in This Video.

This 5-minute critique pulls back the curtain on where AI is succeeding and where the biggest challenges remain. Experts expose the gap between market hype and reality: the failure to deploy fully autonomous production agents and the missing human-machine interface for non-developers. It’s a challenge to the entire industry.

Graylog V7.0 supports integration with the Model Context Protocol (MCP), which allows large language models (LLMs) to access and interact with Graylog data and workflows in real time. Graylog exposes an MCP-compatible endpoint for LLM clients, such as Claude and LM Studio. MCP integration allows Graylog users to interact with their data through LLMs. With MCP, an LLM can connect directly to Graylog as a remote tool interface, performing queries, retrieving system information, and assisting with common administrative or investigative tasks. This capability may make it possible to.

Without strong visibility and governance, local LLMs risk replicating the fragmented, unsupervised sprawl once seen in shadow IT, complicating security postures and making it difficult for organizations to ensure proper oversight and compliance as these powerful AI tools become embedded in daily workflows. To address this challenge, The Splunk Threat Research Team has released the Splunk Technology Add-on for Ollama that provides comprehensive monitoring and observability capabilities specifically designed for local LLM deployments.

At incident.io, we’ve spent the past year shifting how we work to incorporate the AI into both how we build and what we build. The result? AI has become a fundamental pillar of our company. This is the story of how we built reliable AI for reliability itself — reshaping how teams manage and resolve incidents. From early experiments to a company-wide culture of building with AI, this is how we’re redefining incident response for the future.