Evaluation Agent

Evaluation Agent Harness is a robust, production-grade automation pipeline built to ingest, classify, and concurrently execute unstructured task payloads. Designed to overcome the instability of relying entirely on LLMs to follow complex negative formatting constraints, this project shifts the burden of structural predictability from fragile prompt engineering to deterministic code. Built using a highly optimized Python architecture, the agent utilizes a threaded worker pool to process requests concurrently, maintaining tight latency windows. It features a localized heuristic execution engine to handle basic calculations instantly with zero-token remote overhead. For complex tasks, it routes payloads to specialized open-source models (including Kimi and MiniMax via the Fireworks AI API) based on custom intent blueprints. Crucially, the harness features a regex-driven post-processing layer that intercepts verbose reasoning chains and erratic markdown tags, ensuring the final pipeline output is stripped down entirely to clean, executable data. Early development and architectural assumptions were rigorously prototyped using local AMD compute resources running small language models to test model interaction patterns before cloud deployment.