Skip to main content
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›Multi-Agent Architecture
한국어English

Multi-Agent Architecture

Design supervisor, delegation, pipeline, and review patterns for enterprise agents.

Key takeaways

  • Multi-agent systems work only when roles are bounded and communication is structured; otherwise they create hidden state and hard debugging.
  • Common patterns are supervisor, pipeline, parallel research, reviewer, and human gate, each suited to a different coordination need.
  • Give each agent a role, input schema, output schema, and tool scope, and keep shared state explicit.
  • Log handoffs and decisions, cap recursive delegation, and run deterministic checks before side effects.
  • If an operator cannot explain which agent made which decision, the architecture is too opaque.

Multi-agent systems work when roles are bounded and communication is structured. Without that, they create hidden state and difficult debugging.

Patterns

PatternUse
SupervisorOne controller delegates and validates
PipelineSpecialized agents run in sequence
Parallel researchIndependent agents compare sources or options
ReviewerSeparate agent checks output before action
Human gateOperator approves high-risk transition

Design Rules

  • Give each agent a role, input schema, output schema, and tool scope.
  • Keep shared state explicit.
  • Log handoffs and decisions.
  • Avoid agents recursively delegating without a limit.
  • Use deterministic checks before side effects.

Review Question

Can an operator explain which agent made which decision? If not, the architecture is too opaque.

Related docs

Multi-Agent Workflows

Advanced Codex Usage · Split Codex work across bounded agents while preserving ownership and review.

Agent Architecture

New Brand Marketing Strategy · Design AI-assisted marketing workflows with clear roles, inputs, and review gates.

AI SDK Runtime

Use AI SDK as the application runtime layer for streaming, tools, agents, and telemetry.

Approval Backoffice Automation

Automate backoffice work with explicit approval events, identity, and side-effect controls.

Cloud Tasks

Advanced Codex Usage · Use remote Codex tasks for parallel attempts, long work, and reviewable outputs.

Practical Orchestration Playbook

Apply orchestration patterns to triage, approval queues, alerts, and scheduled reports.

Migration Guide

Move from prototype AI features to a governed Vercel enterprise AI platform.

On this page

PatternsDesign RulesReview Question