Enterprise AI tools built on the Model Context Protocol face a critical runtime decision: persistent connections or stateless execution. Most implementations default to persistent sessions, but a serverless approach treats every tool call as an independent HTTP request. This architecture uses queue-based workers, eliminating server affinity and session state stored in the MCP layer itself.

The distinction matters for production systems handling unpredictable loads. While persistent connections work for streaming, enterprise integrations spend most time waiting on external APIs like Salesforce or Stripe anyway. Stateless execution delivers horizontal scaling through queue depth, guaranteed delivery via distributed queues, and transaction-level audit trails without custom retry logic.

Workato's platform demonstrates this with recipe descriptors instead of deployed applications. Any worker can execute any request, enabling zero-downtime updates and just-in-time allocation. Real-world hotel check-ins show 5.5-second workflows where 98% of time is external API calls, making protocol overhead negligible. The architecture forces idempotency and business identifier usage, improving reliability and human-readable logs.

The key takeaway: production AI needs architectural discipline over protocol features. Stateless design makes retries safe, scaling automatic, and debugging tractable. As agents move beyond experiments, choosing serverless MCP transforms integration layers from technical curiosities into reliable infrastructure.