Large language model (LLM)-based techniques have achieved notable progress in generating harnesses for program fuzzing. However, applying them to arbitrary functions (especially internal functions) \textit{at scale} remains challenging due to the requirement of sophisticated contextual information, such as specification, dependencies, and usage examples. State-of-the-art methods heavily rely on static or incomplete context provisioning, causing failure of generating functional harnesses. Furthermore, LLMs tend to exploit harness validation metrics, producing plausible yet logically useless code. % Therefore, harness generation across large and diverse projects continues to face challenges in reliable compilation, robust code retrieval, and comprehensive validation. To address these challenges, we present HarnessAgent, a tool-augmented agentic framework that achieves fully automated, scalable harness construction over hundreds of OSS-Fuzz targets. HarnessAgent introduces three key innovations: 1) a rule-based strategy to identify and minimize various compilation errors; 2) a hybrid tool pool for precise and robust symbol source code retrieval; and 3) an enhanced harness validation pipeline that detects fake definitions. We evaluate HarnessAgent on 243 target functions from OSS-Fuzz projects (65 C projects and 178 C++ projects). It improves the three-shot success rate by approximately 20\% compared to state-of-the-art techniques, reaching 87\% for C and 81\% for C++. Our one-hour fuzzing results show that more than 75\% of the harnesses generated by HarnessAgent increase the target function coverage, surpassing the baselines by over 10\%. In addition, the hybrid tool-pool system of HarnessAgent achieves a response rate of over 90\% for source code retrieval, outperforming Fuzz Introspector by more than 30\%.