Qwen Team Zhu Da Explains C-End Agent Harness Engineering

Qwen team member Zhu Da described the practical methods behind their consumer agent system. The project launched inside the Qwen app in January 2026. It handles complex tasks such as information gathering and research analysis. According to the Qwen team announcement, the agent now supports end-to-end market research on new consumer electronics - pulling pricing data, competitor specs, and user reviews in one flow.

The team applied a simple rule set they call multi fast good save. Execution time dropped to one third of the original length. Token spend reached one tenth of comparable overseas products. In one logged scenario, a user asked the agent to plan a three-day business trip: the harness gathered flight options, hotel availability within budget, and local meeting-room bookings simultaneously, finishing in roughly 40 minutes instead of two hours, while using only one-tenth the tokens of comparable overseas agents and preserving full itinerary quality.

Zhu Da stated that prompt work alone no longer meets daily needs. The next step is harness engineering that organizes memory, environment data, tasks, and assistant actions. He added that AIWare engineering will follow.

The consumer agent started as a single entry point for general tasks. Users request help through a capsule interface inside the Qwen app. The system then collects data, runs analysis, and returns results without constant guidance.

Team records show the approach kept output quality steady while lowering resource cost. Search patterns and context controls replaced earlier trial and error steps. Delivery speed improved because the harness limited repeated calls.

Zhu Da framed the shift from prompt work to harness work as a necessary change. Prompt methods left too many loose variables. A harness adds fixed components that stay in place across runs.

The four parts are user memory, environment signals, task system, and assistant loop. Each part runs with clear limits so the agent does not expand beyond the job. Low power use remains the stated goal.

The same harness now feeds an active service layer. Instead of waiting for requests the system can surface reminders or next steps. User memory supplies personal context while environment data supplies current conditions.

Task system entries keep goals visible. The assistant loop checks those goals against fresh signals and decides whether to speak. Zhu Da noted that emotional fit stays the hardest piece because the agent must judge when a suggestion feels welcome rather than intrusive.

Current tests focus on light daily use. The agent proposes calendar checks or document updates only after memory patterns show repeated need. Early logs report fewer unwanted messages than earlier versions.

Zhu Da said the goal stays simple. Enough performance is enough. Extra scale that does not improve user outcome is cut.

One open question is how widely the active layer can spread before users feel watched. The team keeps the memory store on device where possible. Clear toggle controls appear in the app settings.

Competitors including early versions of OpenAI’s Assistants API and several Chinese rivals have reported mixed results; some systems triggered daily briefings too aggressively, causing measurable uninstall spikes within two weeks, while others throttled suggestions so conservatively that active features saw negligible adoption. The Qwen approach tests smaller triggers first and measures response before adding more.

Next milestones include live user studies over the next three months. The team will track how often active suggestions receive follow up action. They will also measure any rise in token spend when the active layer runs alongside manual requests.

Zhu Da expects harness patterns to spread to other products inside the same company. The same four component structure appears reusable once the first version stabilizes.

Readers who build agents can test the same order of steps. Start with fixed memory and task lists before adding live environment feeds. Measure cost and response time after each addition.