DeepSeek outlines Super-agent architecture where AI orchestrates AI

On 2026-10-31, DeepSeek open-sourced an Agent base architecture in which the AI can locate bugs and submit PRs on its own, though the publicly available component structure, performance data, and applicable scenarios remain limited. The same day, the DeepSeek Harness team published a technical article titled Exploring Super-agent Design: Toward More Autonomous Agent Systems, describing the Super-agent architecture they are exploring. In their vision, future AI agents will no longer be confined to a single chat window; they will stay online, handle multiple tasks simultaneously, and proactively act in response to changes in their environment.

Architecturally, a continuously online Router receives inputs from group chats, GitHub, and email, and dispatches multiple Worker agents that each hold their own context to execute tasks in parallel. The team has built an internal Super-agent instance on top of DeepSeek Harness and uses it for day-to-day development work. For example, when a team member reports a software problem in a group chat, the agent can spot the potential defect even if nobody @s it, try to reproduce it in the background, look for a fix, and submit a GitHub Pull Request for review. A single task can also carry over between group chat and GitHub: the developer agrees on an approach in chat, then revises the requirements in a GitHub comment, and the agent follows the change and adjusts its direction.

At present, Super-agent remains an engineering-focused architecture exploration, with some designs already implemented in open-source prototypes but not yet a standalone product formally released by DeepSeek. Coding-agent tools of the past year can already read code, find bugs, run tests, and complete complex software development work. Most agents still work session by session, and the reports note that although the number of tasks AI can handle at once keeps growing, human attention to supervise them has not kept pace, suggesting that the next bottleneck for AI agents may be humans themselves.