A production-ready reference architecture for multi-agent applications. Features LangGraph stateful orchestration, conditional HITL routing, isolated tool execution, and unified safety constraints.
Authentication & tenant context
Safety, PII, scope & permission checks
Reads graph state & routes intent
Session state, context & tool results
Vector RAG & docs
Action execution & writes
Billing & seat limits
Churn risk mitigation
Policy, hallucination, citation & PII checks
Instant resolution via UI
Human queue & CRM logging
Guided handoff / CTA
Each layer reduces a real failure mode in enterprise AI agent deployments.
Handles user requests from web, Slack, API, webhook, or scheduled jobs. Examples: Next.js, Slack App, API Gateway, Event Grid, Kafka.
Protects access, identity, tenant data, and risky actions. Examples: OAuth, RBAC/ABAC, Azure AD, Okta, policy engine, secrets manager.
Controls routing, state transitions, retries, agent handoffs, and human approval. Examples: LangGraph, Semantic Kernel, AutoGen, custom state machine.
Brings trusted business context into the agent before answering. Examples: vector DB, Azure AI Search, OpenSearch, Pinecone, Databricks, Confluence, Glean.
Executes approved actions through controlled tool interfaces. Examples: MCP servers, REST APIs, internal services, ServiceNow, Salesforce, Jira, Datadog.
Stores session state, graph checkpoints, tool results, user context, and approval status. Examples: LangGraph checkpointing, Redis, Postgres, Cosmos DB.
Tracks logs, traces, latency, cost, failures, and tool-call history for debugging and audits. Examples: LangSmith, Datadog, Grafana, CloudWatch, Azure Monitor.
Measures answer quality, tool accuracy, hallucination risk, and workflow drift. Examples: LangSmith evals, Databricks MLflow, RAGAS, custom test sets, offline grading.
Comprehensive requirements, architecture logic, and operational metrics gathered for production deployment.
We are designing an enterprise AI assistant for a web and mobile experience. The assistant helps users ask questions, retrieve trusted business knowledge, understand account or subscription details, perform approved actions, and escalate complex issues to human support when needed.
The system should support both self-service answers and action-oriented workflows, while keeping security, accuracy, permission checks, and auditability in place.
The Goal: Design a secure, scalable, reliable, observable, and production-ready multi-agent AI assistant using web/mobile entry points, API gateway, authentication, LangGraph-style orchestration, RAG, MCP/tool execution, guardrails, and human-in-the-loop escalation.
Functional requirements describe what the system must do.
Describing how well the system must work across performance, security, and scale.
For a web/mobile AI assistant, we separate normal UI performance from AI workflow performance.
| Area | Ideal Target | Acceptable Target | Notes |
|---|---|---|---|
| Initial page/app shell load | ≤ 2.5 seconds LCP | ≤ 4 seconds | Aligns with Core Web Vitals loading guidance. |
| Server response / TTFB | ≤ 800 ms | ≤ 1.8 seconds | Good backend responsiveness target. |
| UI tap/click feedback | ≤ 100 ms | ≤ 200 ms | User should feel the UI reacted immediately. |
| Interaction responsiveness | ≤ 200 ms INP | ≤ 500 ms | Good Core Web Vitals responsiveness target. |
| Layout stability | CLS ≤ 0.1 | CLS ≤ 0.25 | Avoid content jumping during load. |
| Cached/revisited page reload | ≤ 1 second | ≤ 2 seconds | Use caching, CDN, and client-side hydration. |
| API read request | 300–800 ms | ≤ 1.5 seconds | For normal account/profile/status reads. |
| API write request | ≤ 1.5 seconds | ≤ 3 seconds | For preference updates or simple case creation. |
| Request Type | Ideal Target | Acceptable Target | UX Behavior |
|---|---|---|---|
| Simple greeting/help prompt | ≤ 1 second | ≤ 2 seconds | Return immediately. |
| Basic account/status answer | 2–4 seconds | ≤ 6 seconds | Show loading state if needed. |
| RAG-based answer with citations | 4–7 seconds | ≤ 10 seconds | Stream partial response or show progress. |
| Tool/MCP action workflow | 5–9 seconds | ≤ 15 seconds | Show step status: checking permission, calling tool, validating result. |
| High-risk action requiring approval | Depends on approval | Not fully automated | Ask user/human for confirmation. |
| Human escalation | Case created ≤ 10s | ≤ 20 seconds | Show ticket/case reference if available. |
| Area | Target |
|---|---|
| Web/mobile frontend | 99.9% or higher |
| API/backend availability | 99.9% or higher |
| Critical account/action services | 99.9% or higher |
| AI model fallback path | Required |
| Tool/API retry support | Required |
| Graceful degradation | Required |
| Area | Requirement |
|---|---|
| Users | Support many concurrent web/mobile users. |
| Tenants/accounts | Isolate data and scale per tenant. |
| RAG queries | Scale vector/search independently. |
| Tool calls | Use queueing, rate limits, and retries. |
| Traffic spikes | Use autoscaling and CDN caching. |
“I would scale the frontend through CDN/edge caching, scale the API layer horizontally, scale RAG independently, and isolate long-running tool workflows through queues/async workers.”
cancellation, refund, payment change, downgrade, entitlement change, ownership change, CRM escalation with sensitive data.
| Metric | Target |
|---|---|
| Citation coverage | ≥ 95% |
| Unsupported answer rate | < 2–5% |
| Tool-call success rate | ≥ 95% for stable tools |
| Human escalation accuracy | ≥ 90% |
Track: Request ID, Intent, Agent route, Model/Retrieval/Tool latency, Cost, Errors, Feedback.
Tools: LangSmith, Datadog, CloudWatch, MLflow
Requirements: Log every tool call, write action, approval decision, user confirmation. Track citations. Support retention policies.
Modular architecture: Agents separated by responsibility, versioned prompts, registered tools, refreshable indexes.
Use caching, route simple intents to smaller models, stream responses, track cost per workflow, set budget alerts.
See the interactive visual diagram at the top of this page.
The main idea is that the system first validates the user and request, then uses a stateful orchestrator to route work to the right specialist agent. Knowledge requests go through RAG. Action requests go through MCP tools or internal APIs. Risky workflows require permission checks and human approval. The final response is validated before being returned, and all steps are logged.
“Why was my billing seat limit exceeded, and can you create a support case?”
1.User sends a message through the web or mobile app.
2.Request reaches the API Gateway.
3.Auth layer validates user identity, role, tenant, and account context.
4.Input Guardrail checks prompt injection, PII risk, unsafe content, and permission boundaries.
5.LangGraph StateGraph Supervisor receives the request.
6.Supervisor reads shared graph state, previous conversation context, and session metadata.
7.Supervisor classifies the intent as both an account question and a support action.
8.Account Manager Agent checks billing, subscription, seat usage, entitlement, and account state.
9.Knowledge Agent retrieves billing rules or product policy using RAG if needed.
10.Task Execution Agent prepares a plan to create a support case.
11.System performs permission checks before case creation.
12.If allowed and low risk, the MCP Client calls the approved CRM or case-management tool.
13.If risky, the system asks for confirmation or routes to human approval.
14.Tool result is normalized and returned to the supervisor.
15.Output Validation layer checks final answer for citations, hallucination risk, policy compliance, PII, and action confirmation.
16.User receives a final answer with the reason for the seat-limit issue and the support case status.
17.Logs, traces, tool calls, state updates, and evaluation data are stored.
For this system, I would first gather functional requirements around web/mobile user input, authentication, intent routing, RAG-based answers, tool execution, permission checks, human approval, escalation, and final response validation.
For non-functional requirements, I would define clear targets: initial page load under 2.5s, TTFB under 800ms, interaction response under 200ms, simple AI answers in 1–2s, RAG answers in 4–7s, and tool workflows in 5–9s with progress indicators. I would also define availability, scalability, security, accuracy, observability, auditability, maintainability, and cost-efficiency requirements.
