Insights on API integrations, workflow automation, and building reliable agent systems for enterprise.
A custom MCP server connects your AI agents to internal APIs, legacy ERPs, and proprietary databases through a standard interface. Learn when to build one and how superglue lets you do it in minutes.
As the number of agentic AI applications grows, enterprises must address data access enforcement, auditability, integration reusability, and tool portability — or face compounding risk and technical debt.
We went through three architectural iterations to figure out how our AI agent should apply edits to JSON tool configurations. Here's what we learned about structured output, search & replace, and JSON Patch.
In building integrations using AI, one of the toughest engineering tasks is turning third-party documentation into reliable context for agents. Learn about our approach at superglue.
Why developer tools don't need UIs anymore. With MCP and agents like Cursor, the age of clicking through dashboards is ending. The future is capability-based interfaces that work directly in your editor.
The fundamental difference between agentic tools and API endpoints. Why MCP exists as an abstraction layer that provides capabilities rather than forcing agents to navigate endpoint orchestration.
The reality behind MCP adoption: who builds and maintains MCP servers for legacy enterprise systems? Exploring the challenges of bridging modern AI interfaces with decades-old software.