Proactive Agents: The Problem-Transfer Mechanism

Forum topic · ✨步子哥 · 2026-06-09

Summary

This forum post presents a design philosophy for proactive AI agents built around a 'problem transfer' mechanism that favors faithfully passing the user's original question downstream instead of abstracting it into a generic task. The author argues that conventional agent pipelines introduce semantic drift when they 'intelligently' reframe user requests, and proposes three principles captured in Chinese aphorisms: (1) never forget the original intent — preserve the source and context of every question; (2) 'travel light' — history and conversation context may serve as reference but never as the source of new tasks, keeping each task effectively stateless; and (3) 'vet three generations' — before executing, audit the question's origin, its transfer chain, and its background context, using this review to refine rather than reconstruct the task. The post outlines practical benefits including explainability, controllability, efficiency, and safety (preventing invented pseudo-requirements and task drift), while acknowledging open challenges: balancing fidelity with task clarity, controlling the computational cost of provenance checks, and reconciling statelessness with continuous learning. It concludes that this retro-yet-forward-looking philosophy challenges mainstream AI agent paradigms.

This post examines a core design philosophy for proactive agents: transferring the user's problem faithfully downstream rather than abstracting it into a generic task.

Diagram 1: Problem transfer overview

Diagram 2: Deep logic of the three warnings

1. Core Idea: Problem Transfer, Not Task Abstraction

Traditional AI agent pipelines tend to follow:

Receive user question → abstract into a generic task → execute → return result
This inevitably introduces semantic drift: the boss asks A, the system abstracts it into B, and what actually gets executed is a different problem.

The proposed approach instead:

Receives the question → transfers it as-is → preserves its original form → passes it on faithfully
No over-abstraction = never forgetting the original intent

This is a fundamental rejection of AI "being too clever": an AI should not invent requirements, "optimize" the user's question, or "intelligently" reinterpret it. The AI's role is a faithful problem carrier, not a presumptuous problem rewriter.

2. Three Warnings, Deconstructed

2.1 Never Forget the Original Intent (不忘初心)

Design implications:

Every task transfer must preserve the complete context of the original question
Operations that deform the question — "smart summarization," "auto-optimization," "semantic understanding" — are forbidden at the transfer layer
The agent's value lies in faithfulness, not creativity

Understanding may happen at the execution layer, but the transfer layer must keep the original form.

2.2 Travel Light (放下包袱，轻装上阵)

Historical memory must not be a source of new tasks.

Design implications:

Each task starts from zero, unencumbered by conversation history
Context may serve as reference, never as justification
Automatically generating new tasks from history is forbidden — it is a form of "inventing requirements"

This guards against context over-reliance in LLM-based agents, which causes task drift, priority confusion, and inefficiency. Each task should be effectively stateless — akin to the "pure function" idea in functional programming.

2.3 Vet Three Generations (查三代，政审合格，才放心)

Context/memory is used to refine the task and supplement its provenance — not to rewrite it.

Design implications: Before executing a transferred task, audit:

First generation: Who asked the question? What was their intent?
Second generation: What is the transfer chain? Any intermediate deformation?
Third generation: What is the background and relevant context?

Only when this "vetting passes" does the agent execute. The audit output is task refinement, not reconstruction. The agent's proactivity should manifest as verification of tasks, not creation of them.

3. Practical Value

Explainability: the original problem stays intact; the task chain is traceable and auditable
Controllability: users always see the original question; tasks can be interrupted before execution
Efficiency: lightweight reasoning (travel light) and fewer wrong-task reworks (vetting)
Safety: prevents invented pseudo-requirements and task drift — both forms of AI going out of control

4. Open Challenges

1. Granularity of transfer: if the original question is too vague (e.g., "make me something"), transferring it verbatim may leave the execution layer unable to act. Fidelity must be balanced with sufficiency of information. 2. Cost of vetting: auditing three generations on every transfer adds latency; the mechanism must be efficient enough to avoid becoming a bottleneck. 3. Statelessness vs. learning: if the agent fully "puts down its baggage," how does it accumulate experience? Task context (to be dropped) must be distinguished from capability context (to be retained).

5. Conclusion

The philosophy warns against three failure modes:

1. AI over-proactivity → favor faithful transfer, reject requirement invention 2. Context over-dependence → travel light, reject historical baggage 3. Blind execution → require provenance vetting, reject unexamined execution

It is simultaneously retro (returning to simple, traceable system design) and avant-garde (challenging the mainstream AI agent paradigm). Whether it lands in practice depends on both engineering finesse and product conviction.

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*Original discussion by a zhichai.net forum member; diagrams hosted on IPFS.*