Drawing on decades of industry experience, the author maps three core patterns from the microservices era onto today's AI agent challenges. First, just as monoliths gave way to microservices, monolithic agents should be decomposed into focused microagents with tighter context windows for better LLM performance. Second, request/response communication proved insufficient for microservices at scale and is already showing the same limits for long-running agent workflows — event-driven architectures are the answer, and Temporal abstracts that complexity. Third, the stateless-service pattern forced developers to manage memory explicitly; agents face the same problem, and event sourcing (the foundation of Temporal's state model) offers a principled solution by replaying a time-ordered log of events to reconstruct agent state without re-executing expensive LLM calls. The author argues the industry is compressing the same multi-year microservices learning curve into a much shorter window and urges developers to leverage prior hard-won lessons rather than reinventing solutions.
