Active Agent: Build AI in Rails (docs.activeagents.ai)

Active Agent reframes AI components as first-class Rails controllers: “Agents” behave like controllers where you define actions, plug in callbacks, and render views — applying familiar Rails conventions to LLM interactions. Instead of ad-hoc scripts or separate orchestration layers, prompt logic, chaining of steps, pre/post processing, and presentation of model outputs are expressed using Rails’ existing MVC patterns (actions = LLM tasks, callbacks = before/after hooks, views = prompt/response templates). The significance is practical and fast: Rails teams can build, test, and maintain LLM-driven features using idioms they already know, lowering friction for productionizing AI in web apps. Technically this implies clearer separation of concerns (state and persistence via ActiveRecord, realtime via ActionCable, background inference via ActiveJob), reusable callback hooks for context management and safety checks, and view-based templating for reproducible prompts. It also makes it easier to integrate familiar Rails tooling—routing, logging, testing, authentication—into AI workflows, while highlighting areas to watch (prompt security, rate limits, and latency/async handling). For Ruby/Rails shops, Active Agent promises cleaner, more maintainable LLM integration without inventing a new paradigm.