A strong LangChain application begins with a small, explicit workflow. Define the input, the model call, any tool action, and the expected output before adding memory or multiple agents. Structured outputs make downstream behavior easier to validate than free-form text.
Tools should have narrow permissions and clear input schemas. Retrieval systems need source tracking, access controls, and evaluation for irrelevant or misleading context. State should be stored deliberately; “memory” is not automatically reliable knowledge. Add timeouts, retries, and fallbacks around external services.
Testing should include deterministic unit tests for application logic and evaluation sets for model behavior. Traces help identify whether a failure came from data, prompts, tools, or infrastructure. Framework versions change quickly, so isolate framework-specific code and review upgrade notes. A successful AI application is controlled, observable, and useful – not merely connected to a model.