Ejentum - Reasoning Harness

Positions itself as a guardrail layer you wrap around an AI agent to enforce more rigorous, less sycophantic reasoning. The core pitch is that agents trained on RLHF tend to flatter and fabricate when uncertain, and a reasoning harness at inference time can catch and constrain that behavior before it hits production. The concept is legitimate — this is a real and underserved problem for anyone shipping agentic workflows. The reservation is that the landing page is thin on implementation detail: it is not clear whether this is a prompt scaffolding layer, a fine-tuned model, an SDK, or all three. At a score of 8 it has not gotten much traction yet, which could mean it is early or it could mean the problem is harder than the pitch suggests. Worth bookmarking and revisiting once there is a concrete integration guide or some public benchmark against a baseline agent. -> Best for: AI engineer building multi-step agent pipelines where output reliability matters