Filed under beijing
Every brief tagged "beijing".
- § 01 · AI-AGENTS
Tokens Meter Input, Not Value: Hong Yanqing on Beijing's Agent Measures (Part 4 of 4)
Part 4, closing Hong Yanqing's commentary on the Several Measures of Beijing Municipality on Accelerating Agent-Led Development (北京市关于加快智能体引领发展的若干措施, 京发改〔2026〕1185号). The Measures' Article 6 proposes a Token (词元) economy — Token-as-a-Service, Agent-as-a-Service, Results-as-a-Service, and a shift from billing by Token consumption to value-based billing; Article 8 funds 'Token factories' and Token vouchers. Hong draws the line the policy still needs: Tokens measure the consumption of intelligent means of production, not the value of intelligent products. Tokenization differs across models; a task's full cost includes tool calls, memory storage, human review, and failed retries; and Token volume has no fixed ratio to task value — so treating Token throughput as industrial performance rewards long contexts, loops, and retries. His alternative is a five-layer evidence chain (resource input → system capability → valid task results → process results → enterprise and social value), a cost-per-valid-completed-task formula that counts review, retries, and expected risk losses, and an attribution discipline of pre-launch baselines and phased pilots. Outcome billing must be corrected for quality and risk — narrow metrics make customer-service agents rush calls and procurement agents chase price cuts, and vendors can cream-skim easy tasks while humans absorb the hard residue — so projects with unstable task boundaries should blend base, resource, and performance fees rather than jump to pure Results-as-a-Service. Different policy objects need different-layer metrics, mapped onto Part 3's five maturity levels, and fiscal support should pass staged evidence gates: prototypes may fail, pilots must beat baselines in real business, demonstrations must replicate at acceptable cost, and commercial-stage projects must survive subsidy taper — with prompt exit for projects that stop producing new evidence.
- § 02 · AI-AGENTS
Five Levels of Agent-Reshaped Enterprise Process: Hong Yanqing on Beijing's Agent Measures (Part 3 of 4)
Part 3 of Hong Yanqing's commentary on the Several Measures of Beijing Municipality on Accelerating Agent-Led Development (北京市关于加快智能体引领发展的若干措施, 京发改〔2026〕1185号). Article 3 of the Measures calls on enterprises to 'restructure core business processes' around agents — but using an agent and having processes reshaped by agents are different things. Hong proposes a five-level maturity scale: (1) tool assistance — the person stays in the process, the agent stays outside it; (2) step embedding — the agent enters positions and single workflow nodes inside existing software; (3) bounded closed loop — the agent independently completes a bounded task with its own identity, scoped permissions, and human approval at defined checkpoints, the stage where Part 2's security governance becomes a production precondition and where value-based billing first becomes realistic; (4) end-to-end orchestration — the agent coordinates multiple systems, departments, and roles around a complete business outcome, forcing enterprises to name end-to-end process owners and re-align departmental KPIs; (5) native restructuring — the enterprise redesigns processes, organization, and business model around a new human-agent division of labor, the level that OPCs, Results-as-a-Service, and AI-native software presuppose. Maturity is measured by how much process responsibility changed — task units, data and system permissions, the human role, evaluation units, organizational accountability — not by agent count, automation rate, or architectural complexity; different processes have different legitimate endpoints, and high-risk decisions may properly keep a human decision-maker forever. He closes by mapping each of the ten articles to the levels it serves and proposing that Beijing's scenario lists, funding, and security requirements be allocated by target maturity level.
- § 03 · AI-AGENTS
Why Would an Enterprise Dare Hand Tasks to an Agent? Hong Yanqing on Security Governance in Beijing's Agent Measures (Part 2 of 4)
Part 2 of Hong Yanqing's commentary on the Several Measures of Beijing Municipality on Accelerating Agent-Led Development (北京市关于加快智能体引领发展的若干措施, 京发改〔2026〕1185号). The Measures assign security governance to Article 7 — graded-and-categorized regulation, regularized crackdowns on malicious misuse, AI industry legislation, security-service platforms, ranges, and a trusted sandbox. Hong argues security cannot be one measure among ten: an enterprise that adopts an agent is not buying content-generation software but delegating tasks, data, system permissions, and the power to act externally to a technical system, and that delegation only continues if the agent's action boundary can be limited, its running state observed, its abnormal behavior halted, its errors remedied, its key steps traced, and its final responsibility assigned. He walks the other nine articles showing how each presupposes this 'trusted delegation' — self-evolution needs version governance and rollback; task persistence needs budget caps, retry limits, and human takeover; long-term memory is data processing and data residency, not a free 'data flywheel'; tool calling turns identity and permissions into the core problem; multi-agent skill markets stretch the responsibility chain — and proposes four foundational institutions: action-and-consequence-based agent classification, a trusted-delegation baseline for production agents, security capability as public infrastructure, and security evidence as a condition of fiscal support, procurement, and benchmark-scenario acceptance. Security governance, he concludes, is itself a form of productive capacity: it is what makes enterprises willing to open data, systems, and permissions at all.
- § 04 · AI-AGENTS
How Agents Actually Enter the Enterprise: Hong Yanqing on Beijing's Agent-Led Development Measures (Part 1 of 4)
Part 1 of Hong Yanqing's three-part commentary on the Several Measures of Beijing Municipality on Accelerating Agent-Led Development (北京市关于加快智能体引领发展的若干措施, 京发改〔2026〕1185号, issued 21 July 2026). Hong maps agent supply along two axes — who builds and operates (enterprise self-build, standardized third-party products, joint co-construction with forward-deployed engineers, public/industry shared platforms) and how capability is delivered (whole solutions, componentized assembly via skill marketplaces, embedded in existing software and terminals) — and argues Beijing has covered supply almost completely. What the Measures have not yet answered is adoption: enterprise demand is not 'an agent' but a definable, delegable, verifiable task, and between agent supply and enterprise production processes stand six institutional thresholds — unformed procurement-ready demand, processes that lack the standardization agents require, blocked access to data and tools, missing authorization and responsibility regimes, procurement and acceptance mechanisms built for conventional software, and the absence of migration and exit capability. His prescription: the next phase of Beijing agent policy should pivot from expanding supply to promoting adoption — maturity assessment and process diagnosis, open and non-discriminatory agent access to enterprise software, capability-permission-responsibility inventories, first-purchase programs tied to real production tasks, staged funding tied to task outcomes rather than Token volume, and risk-sharing, insurance, and business-continuity mechanisms for early adopters.