Empire Architecture
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About this skill
Problem It Solves
A single agent often mixes retrieval, computation, drafting, review, and safety checks into one prompt, making context grow and failure hard to isolate. Empire Architecture uses a Three Excellencies and Nine Ministers metaphor to split work across agent tiers: the chancellor routes and schedules tasks, advisory/executive agents perform specialized work, the Hanlin court handles knowledge retrieval, and the Embroidered Uniform Guard performs audit.
How It Works
- Tag routing: agents carry tags, and the chancellor selects nodes by keywords, reducing unrelated prompt load.
- Model tiers: decision nodes use
promodels, while execution and audit nodes can useflashmodels to control token usage. - Task queue: supports priority, a 90s timeout, automatic retries with exponential backoff, and circuit breaking after repeated failures.
- Knowledge and memory: supports local RAG, Feishu, Notion, Tencent Cloud knowledge engines, and other sources; agent memory keeps recent context and high-importance items.
- Safety controls: pre-task sensitive keyword checks, post-task audit, violation classification, and voting.
Limits
It fits multi-step, multi-role workflows such as weather analysis, research, report drafting, and code review. It is less useful for one-off simple questions or strictly real-time tasks. If knowledge-source permissions, model settings, or agent tags are incomplete, routing quality and audit results may degrade.
Use Cases
- For rain analysis, split retrieval, calculation, and drafting across agents and merge a regional rainfall table.
- Pull material from Feishu, Notion, or local RAG, draft a report, and use a review agent to check wording and citations.
- Run pre-task sensitive keyword checks, then use an audit agent to produce a compliance conclusion after completion.
- Use a task queue to set priority, timeout, retries, and circuit breaking when invoking many agents in batch.
Best For
- AI engineers who want to split complex tasks across agents and inspect node inputs.
- Engineers who need to turn Feishu, Notion, or local RAG material into generated results.
- Application security engineers who need pre-task sensitive checks and post-task audits.
- Technical leads evaluating multi-agent orchestration and configuring model tiers or knowledge sources.
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