The Model Context Protocol (MCP) is emerging as a standard for connecting AI agents with external tools. While LLM-based tools are flexible, their non-deterministic behavior makes it difficult to meet requirements for stability, repeatability, and auditability in production systems. In such cases, traditional machine learning models provide more predictable inference and are often preferred for system-critical decisions.

However, ML-powered tools are not free from challenges. Latency, failures, and integration issues can propagate through MCP systems and impact agent behavior, leading to inconsistent decisions, repeated retries, or silent failures.

This session explores what happens when MCP tools fail and how to design systems that remain reliable under such conditions. We present a reproducible demo of failure scenarios, including latency spikes and unstable predictions, and show their impact on agent behavior. We then introduce practical design patterns—such as timeout and retry strategies and observability—to build reliable MCP-based systems.

Attendees will gain concrete techniques to design trustworthy AI systems that remain stable in real-world environments.