Autonomous coding agents are increasingly expected to solve complex, real-world API tasks involving multiple services, dependencies, and alternative solution paths. However, most existing benchmarks, including SOCBench-D, implicitly assume simplified one-to-one task–solution mappings and lack support for evaluating agentic behavior in realistic many-to-many (n:m) settings. As a result, current evaluations fail to capture whether an agent truly understands which APIs are required, how they should be combined, and which endpoints should be avoided. We present SOCBench Runner, a Green Agent that transforms SOCBench-D, a benchmark for evaluating automated REST API integration coding, into a fully agentic, reproducible benchmark within the AgentBeats platform. The Green Agent orchestrates evaluations for multiple Purple Agents that autonomously generate Python code to solve natural-language API tasks. Instead of relying solely on execution success, our approach performs static code analysis to extract all referenced API endpoints and evaluates performance using precision, recall, and F1 scores over task-specific ground-truth API sets. The benchmark supports a wide range of scenarios, including graded difficulty levels (easy, medium, hard), retrieval-augmented generation (RAG) settings, and real-world REST API tasks adapted from RestBench. This design enables fine-grained measurement of endpoint selection accuracy, coverage, overuse, and task completion across diverse domains. By agentifying SOCBench-D and explicitly targeting the n:m task–API evaluation gap, our framework establishes a standardized and extensible benchmark for autonomous coding agents. It provides actionable insights into agents’ ability to reason about API ecosystems, retrieve relevant specifications, and generate correct, efficient code, advancing the evaluation of LLM-driven software development in realistic, production-oriented settings.