As MCP adoption expands, many teams encounter practical challenges when moving from prototypes to production systems. This session focuses on two recurring issues observed while integrating MCP tools into custom AI agents at scale.
An agent-agnostic integration pattern is presented where MCP tools are loaded via configuration and the open MCP library, without coupling to specific agent frameworks such as Claude, Haystack, or Google Agent ADK. By introducing an adapter layer between agents and MCP clients, the system maintains strict protocol boundaries while enabling flexible agent implementations.
MCP session reuse and caching in real deployments is examined. While connecting a single MCP server to cached agents, unintended session re-creation was observed when requests used different API keys, resulting in increased latency and resource utilization. The underlying session lifecycle, how this behavior emerges, and how leveraging MCP’s existing HTTPX client factory mechanisms enables safe reuse of sessions without spawning new agent instances are explained.
The talk emphasizes concrete architectural decisions, protocol-aligned engineering, and performance-driven outcomes.