Building a Production‑Ready Enterprise AI Q&A Platform with AgentScope Java and DashScope

Problem Statement

Simple "call the model" approaches are insufficient for enterprise AI Q&A because they lack trustworthy knowledge, governance, concurrency control, and maintainability.

System Goals

Provide reliable answers based on verified corporate knowledge.

Enable observable and governable reasoning chains.

Support high‑throughput, low‑latency operation.

Offer reusable, extensible components for multiple scenarios.

Architecture

A layered design separates concerns:

┌───────────────────────┐
│ User Access Layer      │
│ Web / App / DingTalk   │
└───────────────────────┘
        │
        ▼
┌───────────────────────┐
│ API Gateway Layer     │
│ Auth, Tenant, Rate‑lim│
└───────────────────────┘
        │
        ▼
┌───────────────────────┐
│ AI Orchestration Layer│
│ Session, Prompt, Router│
│ Workflow, Memory      │
└───────────────────────┘
   │      │      │
   ▼      ▼      ▼
┌─────┐ ┌─────┐ ┌─────┐
│ FAQ │ │ RAG │ │ Action│
└─────┘ └─────┘ └─────┘
        │
        ▼
┌───────────────────────┐
│ Capability Support    │
│ DashScope LLM, VectorDB│
│ Redis, MQ, Biz APIs   │
└───────────────────────┘

Key Technical Components

AgentScope Java abstractions : Agent, Tool, Memory, Workflow, Router separate model calls from business logic.

RAG pipeline : Document parsing → metadata extraction → vectorization → cacheable retrieval → query rewriting → reranking → context compression → citation‑enhanced generation.

Tool layer : Strongly typed, permission‑checked wrappers for HR, ITSM, etc., annotated with @Tool for auto‑registration.

Memory : Redis‑backed conversation store with configurable window, token limits, and optional summarization.

Concurrency handling : Thread‑pool isolation, async parallel loading of retrieval, history, and user profile, fast/slow lane routing, Resilience4j circuit‑breaker and timeout policies.

Observability : Micrometer metrics (request counts, latency per component, cache hit rate, token usage), structured logs with traceId/sessionId/userId, Prometheus exposure.

Resilience : Separate circuit‑breakers for HR and ITSM clients, time‑limiters, fallback messages, graceful degradation to FAQ cache.

Implementation Highlights

Spring Boot controllers expose synchronous JSON endpoints and Server‑Sent Events (SSE) for streaming token output, improving perceived latency.

@RestController
@RequestMapping("/api/v1/chat")
@RequiredArgsConstructor
public class ChatController {
    private final ChatApplicationService chatApplicationService;

    @PostMapping
    public ChatResponse chat(@Valid @RequestBody ChatRequest request) {
        String traceId = UUID.randomUUID().toString();
        MDC.put("traceId", traceId);
        try {
            return chatApplicationService.chat(buildCommand(traceId, request));
        } finally {
            MDC.clear();
        }
    }

    @PostMapping(path = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public SseEmitter stream(@Valid @RequestBody ChatRequest request) {
        String traceId = UUID.randomUUID().toString();
        SseEmitter emitter = new SseEmitter(30000L);
        chatApplicationService.stream(buildCommand(traceId, request),
                token -> send(emitter, token),
                emitter::completeWithError,
                emitter::complete);
        return emitter;
    }

    private ChatCommand buildCommand(String traceId, ChatRequest request) {
        return ChatCommand.builder()
                .traceId(traceId)
                .sessionId(request.getSessionId())
                .userId(request.getUserId())
                .department(request.getDepartment())
                .message(request.getMessage())
                .build();
    }

    private void send(SseEmitter emitter, String token) {
        try {
            emitter.send(SseEmitter.event().name("token").data(token));
        } catch (IOException e) {
            emitter.completeWithError(e);
        }
    }
}

The IntentRouter uses fast keyword rules combined with a lightweight classifier to route requests, reducing unnecessary LLM calls.

public IntentType route(ChatCommand command) {
    String msg = command.getMessage().trim();
    if (containsAny(msg, "你好", "hello", "hi", "在吗")) return IntentType.SMALL_TALK;
    if (containsAny(msg, "年假", "报销", "制度", "流程", "政策", "规范")) return IntentType.POLICY_RAG;
    if (containsAny(msg, "余额", "工号", "员工", "部门")) return IntentType.EMPLOYEE_QUERY;
    if (containsAny(msg, "创建工单", "提工单", "申请", "审批", "报修")) return IntentType.ACTION_REQUEST;
    return IntentType.COMPLEX;
}

Production‑Ready Practices

Layered caching for FAQ, vector retrieval, and document summaries.

Thread‑pool tuning for CPU‑bound LLM calls vs. I/O‑bound tool calls.

Graceful degradation: fallback to FAQ cache or cached summaries when retrieval or model calls fail.

Security checks on tool calls: identity propagation, permission validation, audit logging, idempotency.

Comprehensive testing: unit tests for routing and post‑processing, integration tests for RAG and Redis, contract tests for external APIs, regression suite with a golden question set, and load testing.

Deployment & Scaling

Dockerfile and Kubernetes manifests provide containerization, resource limits, secret management, and horizontal scaling. The API tier is stateless; all state resides in Redis and external services, enabling easy scaling.

Roadmap

Stage the migration from a simple demo to a full platform: start with a single FAQ/RAG agent, add secure tool integration, introduce multi‑agent orchestration, then apply high‑concurrency optimizations, observability, and multi‑tenant isolation.