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Last updated: May 2026 An Oracle MCP server is a service that exposes Oracle Database data as tools an AI agent can call through the Model Context Protocol (MCP). Rather than handing an LLM direct credentials to a database holding ERP, financial, or healthcare records, you put an MCP server between the agent and Oracle.

Last updated: May 2026 A Snowflake MCP server is a service that exposes Snowflake warehouses as tools an AI agent can call through the Model Context Protocol (MCP). It sits between AI clients like Claude or ChatGPT and your Snowflake data, translating discoverable tool calls into governed SQL — with row access policies, dynamic data masking, query budgets, and audit logging applied automatically.

Xray has won the Atlassian Marketplace Partner of the Year 2026: Software Collection Apps, recognizing its continued contribution to the Atlassian Marketplace and its role in supporting software teams with native test management inside Jira. The award was announced in Anaheim during Atlassian Team ’26, as part of the Atlassian Marketplace Partner Awards celebrations recognizing outstanding partners across the Atlassian ecosystem.

It's easy to get caught up in the AI hype, but excitement can stop us from seeing the practical steps needed to make AI truly work. At Appian, we recognize that AI is at its most powerful within a process. Before you get to embedding AI in process, however, you must determine if AI is what you need.

“Garbage in, garbage out.” We are not the ones who said this, George Fuechsel did. But when we are talking about AI today, it is hard not to repeat it. We spend a lot of time discussing what AI can do, the outputs, the predictions, the impact it can create. Much less attention goes to what is actually going into these systems.

The challenge of any aerospace company is to deliver new capabilities without compromising safety, reliability, or precision. At our current juncture, legacy technology runs into conflict with modern tool stacks. Artificial intelligence (AI) creates fissures in compliance and auditability, and innovation and productivity gains come at a cost of greater complexity. Despite these seismic shifts, the central question remains the same.

Engineering is in the middle of an almighty shift. Thanks to AI code-generation solutions, Engineers are being asked to take on a different and wider set of responsibilities in order to be more productive. It’s what’s increasingly being coined as Agentic Engineering - using AI agents to accelerate engineering & operations work while maintaining human oversight, quality and rigour.

Every customer interaction generates signals that matter—a failed checkout, repeated form errors, a frustrated support call, a confusing AI agent exchange, or an unresolved email thread. Individually, these are isolated events. Connected, they reveal customer intent, friction points, operational risk, and opportunities for action.

As regulatory frameworks such as the General Data Protection Regulation (GDPR), Digital Operational Resilience Act (DORA), and Network and Information Security Directive 2 (NIS2) converge with the US Clarifying Lawful Overseas Use of Data Act (CLOUD Act), contractual assurances are no longer a sufficient defense. For senior leadership, digital sovereignty has evolved from a compliance checkbox into a core architectural requirement.

The online learning explosion has exposed a massive gap… The numbers are staggering. The global e-learning market, valued at approximately USD 299.67 billion in 2024, is projected to surpass USD 842.64 billion by 2030, growing at a CAGR of 19.0%.