AI Applications
Agent Harness Engine: Evaluating Tool-Using Agents Under Controlled Context
A harness engine makes tool-using agents observable and repeatable by controlling task protocols, context policy, tool traces, and evaluation signals.
Agent Harness Engine: Evaluating Tool Using Agents Under Controlled Context Agent systems become difficult to reason about when the model, tools, memory, retrieval, and evaluator are all allowed to move at the same time. A harness engine gives the system a narrower surface: it defines the task protocol, controls the context supplied to the model, captures tool calls, and turns each run into comparable evidence. The goal is not to make an agent look impressive in a single transcript. The goal is to make agent behavior observable, repeatable, and debuggable enough that engineering decisions can be made from traces rather than anecdotes. 1. Problem Boundary Tool using agents fail in ways that ordinary request response applications do not. A model can call the right tool with the wrong argument, retrieve a plausible but irrelevant document, stop early after partial progress, or loop because...