From Scenario Abstraction to an AI Assistant Production Line: Scalable Architecture and Prompt Plug‑In Design

Background and Problem Statement

When every business unit needs its own AI assistant, teams repeatedly implement similar pipelines—designing prompts, integrating tools, and validating results—leading to high development cost and low reuse. The article argues for a shift from a "workshop" model to an "AI assistant factory" that abstracts common patterns.

1. Scenario Abstraction

Through observation of daily work, the authors identify that roughly 80% of tasks fall into four high‑frequency scenarios:

Complex Commands : Multi‑step tool selection, planning, and secure execution.

Knowledge Q&A : Multi‑modal retrieval and generation of up‑to‑date knowledge.

Problem Diagnosis : Root‑cause analysis (RCA) from symptom to cause.

Simple (Minimal) Scenarios : Direct use of core capabilities with minimal orchestration.

Each scenario has distinct goals, challenges, and technical requirements.

2. Technical Solutions for the Four Scenarios

2.1 Complex Command Scenario

Goal : From a user instruction, locate and orchestrate the appropriate tools to accomplish the task.

Key Components :

IntentResult Model : Parses a natural‑language command into a structured representation (who, what, where, when, etc.).

FSWW Algorithm (Fused Subspace with Word Weights) : Weights key action and object tokens to retrieve the most relevant tools from a unified ToolEssentialModel pool.

Tool Chain Planning : Performs target‑tool selection, dependency analysis, upstream data retrieval, and constructs an execution chain (e.g., query user ID → issue coupon).

Four‑Layer Security Checks : Environment validation, read/write type enforcement, capability matching, and permission verification.

The workflow consists of preprocessing, intent recognition, intent handling, tool recall, reasoning & execution, and result summarization.

2.2 Knowledge Q&A Scenario

Goal : Retrieve relevant text and images from massive corpora, generate up‑to‑date answers, and return associated visuals.

Pipeline :

Preprocessing of the query.

Query rewriting.

Knowledge cache replacement.

Context generation.

Answer generation.

Image filling.

The system splits documents into logical text blocks, extracts semantic summaries from images via a dedicated image‑understanding agent, and stores text and image embeddings together, enabling simultaneous text‑and‑image retrieval.

2.3 Diagnosis Scenario

Goal : Perform RCA (symptom → possible cause → root cause) using a ReAct‑style agent.

Process :

Intent classification (business query vs. troubleshooting).

ReAct reasoning with a ToolExecutionChain that plans actions without immediate execution.

Iterative loops that incorporate tool results, observation, and historical dialogue.

Two executor variants: ReActExecutor (fast, Thought→Action) and ReActObservationExecutor (full Thought→Action→Observation) for complex multi‑step tasks.

Dynamic knowledge selection decides whether to retain prior context or introduce fresh information based on topic switch, dialog act, and novelty signals.

2.4 Simple (Minimal) Scenario

Designed for rapid onboarding, this flow reuses the multimodal preprocessor, shared tool pool, unified RAG knowledge base, and standard security model, allowing users to create lightweight assistants with a few clicks.

3. Prompt Plug‑In Architecture

Prompts are treated as executable code. The authors separate a framework prompt (core semantics, output schema, step‑by‑step logic) from business‑customizable sections (intent taxonomy, domain‑specific vocabularies, case statements). This modular design enables:

Standardized role definition and responsibilities.

Consistent output models (e.g., IntentResult).

Extensible customization for intent classification, normalization, and special‑case handling.

Examples of the prompt structure include role description, output format, deep semantic extraction, intent classification, and normalization rules.

4. Platform Implementation – The "Assistant Factory"

The solution is materialized in the "Zhihui Space" platform, offering a four‑step assistant creation experience:

One‑click template selection (e.g., complex command, knowledge QA, diagnosis, minimal).

Three‑panel configuration for intent definition, tool mapping, and security settings.

Instant activation – saved configurations become immediately usable.

The platform also provides advanced editing modes for senior users to fine‑tune intent models and diagnosis strategies, visualizing workflow diagrams and enabling custom prompt insertion.

5. Conclusions and Future Directions

The authors summarize three core takeaways:

Methodology : Scenario abstraction → solution codification → productization.

Technical Stack : Template + prompt framework + business customization balances standardization and flexibility.

Product Impact : Lowers the barrier to building AI assistants while maintaining high quality.

Future work includes expanding to decision‑making and data‑analysis scenarios, automating case‑study extraction and intent generation, and evolving backend agents toward greater autonomy.

Overall, the article presents a comprehensive, reproducible blueprint for scaling AI assistant development across an enterprise.