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dbForge AI Assistant Overview for SQL Developers

Meet dbForge AI Assistant — your AI-powered copilot for SQL coding, query optimization, explanations, troubleshooting, and conversion of natural language to SQL code. This overview shows how the Assistant works inside dbForge products and how it helps developers, DBAs, analysts, and teams increase productivity. Key features: Context-aware SQL generation Conversion of natural language to SQL Query optimization SQL explanations Troubleshooting and error insights AI chat for SQL-related questions Optional web search.

AI Observability in 2026: Why the data layer means everything

If there was ever a year for AI observability, it was 2025. Vendors released assistants to cover a variety of use cases. Coralogix released the first agent (distinct from assistants!), Olly, an autonomous, multi-agent observability platform. The direction of travel is clear, but many vendors and users are about to run into some significant problems with their data layer.

How agentic IT operations lay the foundations for SRE success at scale

When something breaks in a modern digital service, customers feel it instantly. Pages stall, requests time out, and carts are abandoned, while frustration grows long before a root cause is identified. What the world never sees is the engineering effort required to keep these systems healthy in the first place. Site Reliability Engineers (SREs) carry that responsibility every day.

Accelerating IT Transformation with Agentic AI

As enterprises face increasing pressure to manage vast and complex IT environments, the demand for faster and more efficient IT management is rising. Traditional operating methods are proving insufficient, making the adoption of Agentic AI essential for organizations aiming to achieve truly autonomous IT operations. This innovative technology enhances decision-making and enables businesses to remain agile in a rapidly evolving digital landscape.

How to Use MCP to Optimize Your Graylog Security Detections

Security teams face a critical question: “What logs should we collect, and what detections should we enable to protect against threats targeting our industry?” For a bank in the northeast, this isn’t academic. Threat groups like FIN7, Lazarus Group, and Carbanak specifically target financial institutions with sophisticated attacks ranging from SWIFT compromise to ransomware.

How AI in Asset Management Is Transforming Asset Addition in 2026

AI in asset management is redefining how organizations add validate and govern assets in 2026. What was once a slow manual and error prone process is now becoming intelligent automated and highly accurate. As enterprises scale across locations and asset types the pressure to maintain clean asset data from day one has increased dramatically. This is where AI in asset management is making a measurable impact. In the first hundred words itself it is clear that AI in asset management is no longer optional.

How Inkeep Monitors Their AI Agent Framework with SigNoz

AI agents are fundamentally different beasts to monitor compared to traditional applications. A single user request can trigger a cascade of 10+ internal operations: sub-agent transfers, tool executions, LLM calls, API requests, each with unpredictable latency and failure modes. When something goes wrong (and with LLMs, things go wrong in creative ways), you need to see the entire execution flow to debug effectively.

How Forward-Looking Institutions are Benefiting from Agentic AI

Today’s higher education institutions operate complex digital ecosystems that were unimaginable a decade ago. Behind every college lies a portal of interconnected systems for registration, financial aid, course management, and campus services. The students using those systems are digital natives who can order food in seconds on their phones or have packages delivered the same day they order them.