An agent toolkit starts with a workload, not a model list
Choosing capabilities for an AI agent is easier when the starting point is a real task. A research assistant needs evidence it can trace back to pages. A content workflow needs a clear output specification for text, images or video. A practical evaluation starts with a few representative tasks. Write down the required output, run the same model and specification across the options you are comparing, and keep a record of rejected results and retries. The useful cost measure is the total charged cost divided by the number of accepted outputs. Data capabilities need their own acceptance checks. For web research, retain the source URL and retrieval context alongside the answer. For social or SEO data, establish which fields and coverage the task requires before treating a response as complete. BeatAPI is the professional capability layer for any agent, described as OpenRouter for Agents. It provides text, image and video models and shipped Social Data, SEO Data and Web Search capabilit...