Everything in its place: Making agents write correct Temporal applications

Effective use of coding agents for Temporal applications depends less on the chosen AI tool than on supplying reliable context, enforcing quality controls, and agreeing on a clear plan before implementation. The recommended foundation combines the Temporal Developer Skill for vetted guidance, official SDK sample repositories for executable feature-specific patterns, and the Temporal Knowledge Base MCP server for cited answers drawn from documentation and expert resources. Agents should be constrained through test-first development, including isolated Activity, mocked Workflow, integration, Signal, and replay tests, alongside linters, formatters, strict type checking, and direct use of the Temporal CLI to run workflows locally and inspect Event Histories. Detailed, scalable specifications help resolve key architectural decisions such as Workflow versus Activity boundaries, Signals versus Updates, Child Workflows versus Nexus, event-history limits, and Continue-As-New needs. When an agent makes an error, the approach advocates identifying missing context or guardrails, recording the lesson in project instructions or reusable skills, and continually improving those resources. This treats AI agents much like new engineering team members that require onboarding, trusted references, design documents, code review mechanisms, and operational validation, while keeping human engineers responsible for the underlying decisions.

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