The Agentic AI Foundation welcomes agentgateway — an open source gateway purpose-built for MCP, Agent-to-Agent, LLM, and API traffic — as its newest hosted project.
Most MCP tools are designed for programmers. Agents are not programmers. Building tools around tasks and intents, then obsessing over description quality, produces dramatically better selection accuracy and fewer failures at runtime.
Getting an AI agent to work is a different problem from getting it to run in production without someone watching it. Datadog built its first hundred agents across SRE, code generation, and security investigation. The hardest parts had nothing to do with model quality.
When Prime Video's engineering teams started building with agents, they followed a pattern that made obvious sense. Give the agent access to the same tools the engineers have. Stand up MCP servers. Let the agent figure out what to call.
Almost all of MCP's current adoption is happening in cloud environments, on large models with generous compute budgets.Manufacturing floors, medical devices, vehicles, smartphones,are where the next wave of AI deployments is headed.
Nick Cooper, Member of Technical Staff at OpenAI and a contributor to the MCP ecosystem, shared that he'd been thinking about this debate for a year, but stopped short of picking a side: "As for whether or not one is better than the other, it's not a decision between one or the other. You clearly…
The operational scale of MCP is significant: Google Cloud maintains the reliability for its MCP servers supporting AlloyDB, Spanner, Cloud SQL, Bigtable, and Firestore. The team's open source MCP Toolbox for Databases has seen rapid adoption, surpassing 13,000 GitHub stars and managing over 20 million tool calls across 40+ databases in the last month.
