China Releases the Implementation Opinion on AI Agents: Guardrails, Classified Governance and 19 Application Scenarios

On May 8, 2026, the Cyberspace Administration of China (CAC), the National Development and Reform Commission (NDRC) and the Ministry of Industry and Information Technology (MIIT), together with relevant parties, officially released the Implementation Opinion on the Standardized Application and Innovative Development of AI Agents (the Opinion), published in full via cac.gov.cn. As a companion to the State Council's Opinion on Deepening the AI+ Initiative, the document deploys 33 measures across six parts — development foundations, safety bottom lines, application traction, innovation ecosystem and safeguard measures — and, for the first time in a national action document, maps out 19 key application scenarios across five directions, marking the industry's transition from technology validation into a new cycle of standardized innovation.

1. Positioning: agents confirmed as the core form of AI adoption

The Opinion opens by defining agents as intelligent systems capable of autonomous perception, memory, decision-making, interaction and execution — an important form of AI products and services that is accelerating its fusion with cyberspace and the physical world. Four basic principles frame the entire document: safety and controllability (safety, reliability and trustworthiness as bottom-line requirements across R&D, deployment and promotion, guarding against systemic risk); orderly regulation (governance that dovetails with existing laws, with industry self-discipline and clear red lines); innovation-driven development (theory, technology and engineering innovation linked, with government-industry-academia-research-application collaboration); and application traction (proceeding from easy to hard around real demand). Bottom-line thinking combined with scenario openness is the key to reading this document.

2. Foundations: technology base and standards in parallel

On technology (Measures 1-2): continuously improve general foundation models, support specialized models for vertical domains, and build a model matrix across scenarios and devices; strengthen high-quality dataset supply; and tackle six key capabilities — task understanding, task planning, tool use, long-term memory, mutual recognition and interconnection, and swarm collaboration. The toolchain measures call for key components spanning perception, memory, decision-making, interaction and execution, full-lifecycle tools for development, testing, deployment and operations, and governance tooling such as adversarial-sample and behavioral-anomaly detection, ensuring discovery, intervention, blocking and recovery capabilities over non-compliant agent behavior.

On standards (Measures 3-4): build standards covering key technologies, products, data exchange, application scenarios, quality evaluation, security and trusted certification; accelerate interface standards between agents and software tools, application services and hardware peripherals; and promote the Agent Interconnection Protocol (AIP) as national and industry standards, with mandatory standards for healthcare, transportation, media and public safety. Most forward-looking is the intelligent Internet layout: researching an architecture, exploring an agent registration platform providing digital identity, discovery, capability declaration and compliance certification services, and leveraging IPv6 for end-to-end communication. Standards-first means interface compliance, interconnection and quality evaluation will become de facto market entry thresholds for agent products.

3. Safety bottom lines: three decision tiers and classified governance

Product principles (Measures 5-7) draw three hard boundaries. First, agent behavior must comply with laws, regulations and mainstream values — preventing the use of data advantages and personification techniques to spread harmful values or exploit users through algorithms, and guarding minors and the elderly against addiction and emotional dependence. Second, the Opinion delineates three decision tiers — decisions reserved to the user, decisions requiring user authorization, and agent-autonomous decisions — with users retaining the right to know and the final say over autonomous decisions, and execution never exceeding the scope of authorization. Third, develop rule-embedding and behavioral guardrail technologies so agents behave lawfully in public, private and dedicated settings, and explore blockchain for verifiable, traceable agent behavior.

Risk defense (Measures 8-10) covers intrinsic security (data poisoning, privacy leakage, algorithm tampering, system vulnerabilities, loss of control), full-cycle supply-chain security norms covering model access, API calls and tool extensions, and standing risk identification, early warning, human-machine collaborative review, and interception capabilities.

The governance framework (Measures 11-14) is the design the industry watches most: classified, tiered governance. Sensitive sectors and key industries such as finance, healthcare, education and public safety will see opening scenarios designated jointly by CAC and sector regulators, with filing, testing and product-recall measures; low-risk domains such as daily entertainment and office work will be governed efficiently through self-assessment, information reporting, distribution-platform management and industry self-discipline. Measure 12 builds a compliance services market (third-party evaluation, mutual recognition of certification and testing results, maturity reports), while Measures 13-14 establish industry self-regulation rules and a credit-rating mechanism with penalties for technology abuse, inducement, false publicity and concealed defects. Classified governance draws a clear compliance cost curve for B2B and B2G delivery: heavy supervision for high-sensitivity industries, light-touch for low-risk scenarios.

4. Application traction: 19 scenarios across five directions

Scientific research (Measures 15-16): agents for theoretical deduction, simulation and emulation, and full-process intelligentization from scheme design through experiment operation, data processing and result analysis. Crucially, Measure 16 (R&D assistance) explicitly calls for developing software-development agents covering requirements analysis, architecture design, code generation and testing across the full lifecycle, and integrating agents with CAD/CAE software for design generation, simulation verification and parameter tuning. Software-development agents are written into a national action document for the first time — the AI software factory moves from frontier exploration to policy-endorsed direction.

Industrial development (Measures 17-21): intelligent manufacturing (dynamic production scheduling, process-parameter optimization, defect recognition, fused with CNC machine tools, industrial robots and automated lines), energy and resources (environment sensing, disaster early warning, power dispatch), transportation (risk early warning, emergency dispatch, network efficiency), agriculture (pest and disease diagnosis, smart greenhouses, intelligent farm machinery) and financial services (credit risk control, transaction monitoring, anti-money-laundering, account security).

Consumption (Measures 22-24): terminal applications (cross-device orchestration across phones, PCs, cars, homes, wearables and consumer robots), culture and tourism (content-creation agents, smart guides, multilingual translation, accessibility) and commercial services (24/7 intelligent customer service plus embodied agents for guiding, cleaning, warehousing and dispensing, with low-cost domestic, elder-care, childcare and disability-assistance services to be explored).

Public well-being (Measures 25-28): education (lesson generation, homework grading, personalized learning — agents must not replace teachers' core teaching decisions), healthcare (imaging analysis, diagnostic reasoning support, surgical scheduling — agents are barred from making diagnosis and treatment decisions independently), human resources (employment promotion, labor arbitration, wage-arrears governance) and information services (topic planning, editing, intelligent review, real-time translation). Assistance rather than replacement is the uniform yardstick for high-sensitivity industries.

Social governance (Measures 29-33): government services (assisted approval, policy consultation, the shift from people seeking services to services finding people), judicial services (evidence review, assisted drafting of legal documents, legal-aid consultation), public safety (monitoring and early warning, emergency rescue dispatch, embodied agents for disaster relief and security patrol), urban governance (intelligent construction, infrastructure safety) and bidding and tendering (full-chain smart supervision).

5. Innovation ecosystem and safeguards

Measures 34-38 advance on three tracks: cultivating open-source innovation around agent frameworks, interaction interfaces and toolchains, with compatibility across open-source chips, operating systems and large models; building collaboration platforms and distribution channels including agent software stores and supply-demand information platforms, with open bidding and challenge-based commissioning; and running pilots in industrial clusters and key industries to create demonstration projects, while leveraging international platforms such as the World AI Conference and the World Internet Conference to cultivate a global ecosystem and guide overseas compliance. Safeguards put CAC, NDRC and MIIT in charge of overall coordination, with an agent development evaluation index system, rolling implementation and dynamic adjustment.

6. KooDa AI's read: three industry judgments

First, software-development agents enter a policy window. The full-lifecycle capabilities named in Measure 16 — requirements analysis, architecture design, code generation, testing and verification — are precisely the core production paradigm of the KooDa AI multi-agent software factory; software R&D sits among industry-development scenarios where landing resistance is low and demonstration effect is strong, making it one of the most active tracks for agent validation and iteration.

Second, compliance capability becomes part of product competitiveness. Full-cycle security norms, filing, testing and recall, and traceable behavior mean that industrial-grade quality systems — full-process audit trails, multi-layer gated review and auditable delivery — shift from differentiators to prerequisites. The KooDa AI software factory's parallel multi-agent review, versioned artifact bus and end-to-end auditability align naturally with the Opinion's governance tooling (Measure 2), behavioral traceability (Measure 7) and standing risk controls (Measure 10).

Third, standards are ecosystem positioning. AIP, the agent registration platform, capability declaration and trusted certification will reshape how the agent industry interconnects; products that align early with standards and mutual recognition will seize the high ground in the next phase of ecosystem division of labor. For a software factory, delivering agent outputs that meet national standards will be the entry ticket to government, enterprise and key-industry supply chains.

Closing

By setting bottom lines through regulation, creating space through innovation and driving adoption through scenarios, the Opinion marks the transition of China's agent industry from wild growth to standardized innovation. KooDa AI will keep tracking supporting rules, mandatory standards and the evaluation and certification system as they land, responding to policy direction with multi-agent software factory engineering practice — helping enterprises fully release AI productivity within a compliant framework.