This article argues that AI agents often mistake model output or tool calls for task completion, but real business completion requires verifiable read-back evidence, idempotency handling for timeouts, explicit acceptance criteria, and structured completion records — not just a 'final_output' signal.
The article argues that fluent AI Q&A fails at multi-step task completion because it lacks persistent service state — problem context, conditions, progress, and exception handling — and suggests evaluating services on continuity rather than answer accuracy.
This survey argues that modern LLM agents should be viewed as a coupled system of a foundational model and an execution harness, analyzes the evolution from prompt engineering to harness engineering, defines six core harness responsibilities, examines task pressures, proposes richer evaluation metrics, and outlines future research directions.
A new SaaS‑Bench study evaluates leading large‑language models across 23 real SaaS applications and 106 multi‑step tasks, revealing that even the best agents complete fewer than four percent of workplace jobs and exposing four fundamental failure modes that keep AI far from replacing human workers.
