When Good Tools Fail: Making MCP Durable With Temporal

Melissa Herrera, a Senior Developer Advocate at Temporal, discusses the challenges and solutions associated with complex agentic workflows, particularly the issue of error compounding in multi-step processes. She argues that treating tools as workflows rather than standalone functions can mitigate these issues, drawing parallels to distributed systems engineering solutions that have existed prior to the AI boom. Temporal's Durable Execution model offers a framework where failures in workflows are recoverable through checkpoints, automatic retries, and self-healing. This approach transforms potentially fragile, error-prone workflows into robust, dependable systems, similar to video game savepoints that prevent players from losing all progress. The architecture of Temporal involves Workflows, Activities, and Workers, enabling deterministic orchestration while handling non-deterministic tasks like API calls. Herrera emphasizes the relevance of these distributed system patterns to AI agents, which face similar challenges due to their dependence on external APIs and complex chains of operations. Temporal's primitives, including Signals and Queries, enhance interaction with long-running processes by allowing human intervention and state inspection without disrupting the workflow. Herrera sees the potential for agents to be treated as tools within this framework, promoting a modular, resilient approach to building complex agentic systems. She highlights that the issues AI engineers face today echo those from earlier distributed systems architectures, reinforcing the importance of durability in modern AI applications.