2025: The AI Agent Year and a New Standard to End the Evaluation Black Box

Background

2025 is positioned as the “year of AI agents” after the State Council issued the “Artificial Intelligence+” Action Plan, aiming for more than 90% adoption of AI agents by 2030. Market research predicts the global AI‑agent market will reach US$11.3 billion in 2025, making AI agents a pivotal technology trend reshaping enterprise operations.

Challenges

Enterprises deploying AI agents encounter a persistent “efficiency black box” because there is no scientific, unified, and quantifiable evaluation framework. This creates three intertwined difficulties:

Selection difficulty: Without a common “capability yardstick,” firms cannot objectively compare agents against their specific business scenarios, leading to costly mis‑fits.

Acceptance without evidence: Lack of measurable performance indicators makes it impossible to prove the business value of an AI agent, hindering investment justification.

Optimization difficulty: Even when performance issues are identified, the absence of a systematic assessment model prevents targeted improvements, leaving agents in a “usable but not optimal” state.

The New Standard

The Zhihhe Standard Center has drafted the Enterprise‑level AI Agent Application Performance Evaluation Specification , the first national group standard focused on AI‑agent applications. It aims to create a “measurement backbone” that guides agents from pilot projects to full‑scale production.

Target Audience

Technology, product, and service providers – for evaluation during R&D, quality management, and performance demonstration.

Application side (enterprise users) – for objective assessment during selection, acceptance, and performance‑based KPI evaluation.

Third‑party testing agencies – to conduct neutral, standardized evaluations and report results.

Supporting units – to feed evaluation outcomes back into industry‑wide technology validation and safety compliance.

Key Content

The specification defines core evaluation activities, methods, and requirements across three lifecycle stages: early‑stage selection verification, mid‑stage project acceptance, and post‑deployment operational optimization, forming a closed‑loop management process.

It introduces four comprehensive dimensions for quantifying AI‑agent performance:

Execution efficiency

Business‑value contribution

System quality attributes

Trustworthiness and compliance

Scenario‑specific evaluation factors and ready‑to‑use report templates are provided, enabling a seamless flow from technical implementation to continuous improvement.

Core Value

Helps technology providers translate technical advantages into clear, credible market competitiveness.

Gives enterprise users a concrete, evidence‑based method to overcome the “selection‑measurement‑optimization” dilemma.

Fosters a healthy ecosystem by establishing a common language for producers, users, academia, and regulators, supporting the deeper integration of AI + industry.

Call for Participation

The standard’s scientific rigor and practical guidance are being refined through an open public solicitation. Cloud service providers, large‑language‑model developers, AI‑agent enterprises, third‑party testing and certification bodies, as well as AI‑security and compliance firms are invited to contribute as drafting units or experts.