#Knowledge Graph (4)

Qoder: Alibaba agentic platform for real work - nine product lines on one knowledge engine

Qoder is the agentic coding platform from Alibaba, positioned officially as an agentic platform for real work: not an editor but an end-to-end loop - understand the task and context, plan, call tools, verify results, iterate toward the deliverable - resting on three stated principles (context engineering, agent autonomy, goal-directed loops), with nine product lines sharing one knowledge engine (desktop Qoder and Qoder IDE coexisting rather than replacing each other, Editor and Quest forms, a JetBrains plugin, Qoder CLI, cloud agents and more); session history and memory are stored separately but can be imported from the IDE. Repo Wiki is generated locally by multiple agents, never uploads the codebase, is off by default and supports Auto Update, Auto Export and Citation back to source locations. Quest has four drives - Agent, Experts, Goal and Spec (convertible to scheduled tasks): Spec runs requirement clarification (multiple choice, with Recommend / Continue / Skip), a structured Spec covering requirements, design, task breakdown and acceptance criteria, human review, execution, then Review/Commit/Push, while Goal takes only the desired outcome and evaluates progress at the end of every round, continuing automatically until met. Two scaled cases: building Qoder with Qoder (10 people, 3 weeks, 500,000 lines of agent code merged into a 4-million-line legacy system, 99% agent-generated, still in production at v1.4.0 with zero incidents; the method is a cognitive base plus Ultra Spec plus Experts cross-review plus a verifier agent filtering hallucinated issues, with humans only deciding SLO definitions and irreversible operations, and each person driving 20-plus Experts tasks a day); and AutoSDK for AMap in-car systems across 20-plus repositories and over a million lines, where the strict first-pass rate went from 37.3% to 61.5% (problem framing cites KoCo-Bench / arXiv:2601.13240v3: general coding reaches 90% Pass@1 while domain code generation reaches only 8.9%). Boundaries: the client and knowledge engine are closed; every scaled number comes from official cases and vendor self-reporting, and self-evidence from a product about itself carries methodological self-interest, none of it independently reproduced; Experts cost per unit is clearly above a single agent (median about 75 versus about 50 Credits) and Credits reset each cycle rather than accumulating. Confidence C (vendor-claim).