6 Rules to Build Better AI Agents with MCP

Building AI Agents and MCP toolchains? The model is rarely the bottleneck — bad architecture is. Overly long tool-call chains, messy context management, and missing fallback logic will break your agent before it ever ships. Whether you're running Claude Opus 4.8 via Claude Code for deep reasoning or orchestrating multimodal flows with Gemini CLI, the fundamentals stay the same: clear tool boundaries, observable intermediate states, and smart model tiering. These 3 cards distill the key practices. And when you're testing across models, XycAi gives you one API to access 200+ models — no extra keys, no friction.