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NewsMaterial4 min readDisconfirmAI Editorial

Alibaba Cloud Engineering Team: Code Written Ten Times Faster, Why Delivery Cycle Only Shortened by 1.8 Days

The Alibaba Cloud engineering team formed an R&D squad of 15 AI Agents, orchestrating them via AgentCore and continuously optimizing performance through AgentLoop. This approach compressed the delivery cycle for specific constrained scenarios from approximately one month to under a week. The article's core argument is that coding accounts for only 10% of the delivery time; the true bottlenecks lie in requirement clarification, architecture constraint transmission, and independent verification.

Verified 2026-11-07 16:07 GMT+8

For a complex feature, the typical team requires about 20 working days from requirement proposal to launch, with coding occupying only two days. Assuming an AI coding tool increases this speed tenfold, reducing two days to 0.2 days, the entire delivery cycle shortens from 20 days to just 18.2 days. In practice records published by the Alibaba Cloud engineering team in November 2026, this arithmetic places the issue on the table: accelerating code writing is not the bottleneck; rather, requirement discussions, solution confirmation, testing and verification, cross-module integration, and release preparation are the actual constraints. Once code is written faster, these preceding and following steps become even more conspicuous.

Their approach involves breaking down the work undertaken by different roles in the R&D process into 15 distinct Agents, which a Leader schedules according to task requirements. Each Agent possesses independent skills, tools, and context, with clear responsibilities: the Researcher understands requirements and the codebase while building a problem evidence graph; the Explorer identifies isomorphic implementations to form candidate solutions; the Spec structures goals, boundaries, and acceptance criteria; the Code Worker implements candidate solutions in independent branches and sessions; Test/Harness executes compilation, testing, and end-to-end verification; Judge/Verify independently selects the best candidate, performs semantic acceptance, and conducts adversarial review; and Deploy prepares the environment and executes release verification.

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Two design choices within this architecture are considered most critical by the authors. First, implementation and verification must be completed by different Agents, ensuring that verification maintains an independent perspective—if the Agent writing the code also judges its own correctness, verification loses its meaning. Second, context across iterations must be preserved—subsequent Agents must be able to continue using information regarding what the previous Agent did, why it was done that way, and what was discovered during verification. If tests fail, the process returns to the original Code Session for continued fixes; if the solution direction is incorrect, Research and Explore are redone with failure evidence; if repeated attempts still do not converge, a Human Gate is triggered to request human intervention for judgment.

Beyond AgentCore, which handles execution, they established AgentLoop as an optimization plane. AgentLoop records the task completion process: which models and tools were called, where failures occurred, how many rounds of rework took place, and whether the final result met requirements. These Trace, Session, and Trajectory data undergo processing through Pipeline, Dataset, Evaluation, and Experiment stages, transforming into reproducible samples, comparable metrics, and repeatedly verifiable experiments. Optimization occurs in three ways: manual, where domain experts define good results, metrics, and evaluation Rubrics; semi-automatic, where the system generates optimization suggestions (identifying tasks consuming excessive Tokens, inefficient steps, or tools consistently reporting errors); and automatic, where experience repositories extract insights from Traces using the RAC method and dynamically inject them into Agents.

In terms of practical effects, validation was conducted in a constrained scenario involving the Trace to Trajectory service, which converts Trace data into trajectory data and frequently encounters new parsing exceptions in production. They selected 19 issues: approximately 10 produced implementations roughly equivalent to those of human experts, while another 5 approached expert level, compressing the delivery cycle from about one month to under a week. In offline experiments, they selected 20 high-quality human-expert Commits from the codebase, reverse-generated requirements to build a Benchmark, and had the Agent team complete these tasks, comparing their results item-by-item against expert implementations.

The authors frankly listed four categories of persistent issues: requirements may be clearly stated, but architectural constraints might not be transmitted effectively (for example, when aiming to extend existing API capabilities, an Agent might choose to add a new API, which is easier to complete for the isolated task but fails to meet the team's design requirements at the global system level); real-world scenarios always bring unforeseen problems (AI application inputs are far more open than traditional software); the same problem may recur (as code, business rules, and runtime environments change, experience must be continuously accumulated to avoid repeating pitfalls); and fixing one problem may impact other tasks (modifying a Skill may pass current use cases but cause regression in others). The existence of these issues indicates that multi-Agent orchestration does not run automatically once the framework is built; it requires continuous observation, adjustment, and accumulation.

Sources

  • alibabacloud.comAlibaba Cloud Community↗
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