Building reliable AI agents requires explicit architectural decisions about infrastructure, model selection, and tooling. Cloud providers offer better long-term integration than direct AI vendors for organizations. API fragmentation across providers can be solved with unification layers like LiteLLM. Small Language Models (SLMs) are often more cost-effective and appropriate for task-oriented agents than large models. Clojure's REPL-driven development and interoperability with both Java and Python ecosystems make it well-suited for AI agent development, enabling faster iteration and seamless integration with existing tooling.
Table of contents
Infrastructure first: where your models live mattersThe fragmentation problem: too many APIsWhy smaller models often work better for agentsWhy Clojure fits this problem spaceFrom theory to practice: the live demonstrationConclusionReferencesSort: