Ejentum - Reasoning Harness

Ejentum exists because your AI agent thinks it's reasoning. It lost the thread five steps ago.

Here's the thing about AI agents in production—they're confident. They're wrong. And they have no idea. Static reasoning baked at build time can't handle what agents encounter at runtime, and that's not a prompt problem. It's architecture. One-size reasoning means your agent applies the same cognitive strategy to a 3-step task and a 30-step chain. It can't shift gears. Three-agent chain at 90% step accuracy gives you 73% end-to-end success. Five agents: 59%. Reasoning errors don't add—they compound, each hop inheriting and amplifying what broke upstream. And here's the killer: reasoning failures don't throw exceptions. By the time wrong output surfaces, the agent has already made three more decisions on top of the bad one. There's no stack trace for cognition.

Ejentum is a tool your agent calls mid-loop. It returns the exact reasoning strategy for the problem in front of it—dynamic reasoning when speed matters, adaptive when the task is novel. They're solving inference-time reasoning correction: a different cognitive ability retrieved for every task, selected at runtime. The problems they identify are real—False Hypothesis Lock-in where agents commit to their first interpretation and never self-correct (on ARC-AGI-3, this is the #1 failure mode of every frontier model), Shallow Stopping where agents reach the first plausible answer and present it as final with no verification, and Attention Dilution where models lose mid-prompt content and your 5,000-token system prompt competes with itself.

Everyone's building agent frameworks. Ejentum is building the nervous system. The boring problems are where the real moat is.