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reopt Handbook
reopt Handbook
Vercel Enterprise AI Platform

Platform Foundation

Platform ArchitectureAI SDK RuntimeAI Gateway Control Plane

Execution Runtime

Workflow Durable ExecutionSandbox Tool RuntimeMCP Enterprise DataQueues and Async Jobs

Prompt and Quality

Prompt Engineering and GuardrailsRAG and Retrieval-Augmented GenerationObservability and Evaluation

Operations

Security GovernanceCost and ReliabilityDeployment and AI CI/CD

Scenarios

Customer Support Agent ArchitectureInternal Research Agent ArchitectureApproval Backoffice AutomationCoding Orchestration

Orchestration Patterns

Graph-Centric OrchestrationPractical Orchestration PlaybookMulti-Agent Architecture

Appendix

Migration GuideReferencesVerificationUpdates
Handbook›Vercel Enterprise AI Platform›Customer Support Agent Architecture
한국어English

Customer Support Agent Architecture

Design customer-facing support agents with retrieval, escalation, fallback, and audit.

Key takeaways

  • Customer support agents need fast answers with strict boundaries, knowing when to answer, when to cite sources, and when to escalate to a human.
  • The architecture layers UI, retrieval over approved docs, AI Gateway routing, a read-only or ticket tool layer, escalation workflow, and observability.
  • Never expose data from other accounts, and cite source material for factual answers.
  • Escalate billing, legal, security, or angry-customer cases, and log low-confidence answers for review.
  • Keep human support able to inspect the full conversation and its sources.

Support agents need fast answers and strict boundaries. The system must know when to answer, when to cite sources, and when to escalate to a human.

Architecture

LayerRole
UICollect question, account context, feedback
RetrievalSearch approved docs, tickets, and knowledge base
AI GatewayRoute by latency, cost, and quality
Tool layerRead-only account lookup or ticket creation
WorkflowEscalation, follow-up, and long cases
ObservabilityTrack resolution, hallucination, and escalation

Guardrails

  • Do not expose data from other accounts.
  • Cite source material for factual answers.
  • Escalate billing, legal, security, or angry customer cases.
  • Log low-confidence answers for review.
  • Keep human support able to inspect the conversation and sources.

Related docs

Knowledge Bases and Internal Wikis

Agentic Documentation · Design KBs for RAG, MCP Resources, freshness, and citation-first answers.

Agent Documentation Security

Agentic Documentation · Reduce prompt injection, MCP tool poisoning, Plugin supply chain risk, and excessive agency.

Prompt Engineering and Guardrails

Design prompts, schemas, safety checks, and review gates for enterprise AI systems.

Secure Architecture

AI Security and Compliance Operations · Design AI systems with policy boundaries, isolation, logging, and safe tool access.

RAG and Retrieval-Augmented Generation

Build retrieval pipelines with indexing, permission filtering, reranking, and evaluation.

Deployment and AI CI/CD

Ship AI systems with preview checks, evaluations, canaries, kill switches, and rollback.

Internal Research Agent Architecture

Build internal research agents for long analysis, artifacts, approval, and source tracking.

On this page

ArchitectureGuardrails