Execute orchestrate multi-agent systems with handoffs, routing, and workflows across AI providers.
Use when building complex AI systems requiring agent collaboration, task delegation, or workflow coordination.
Trigger with phrases like "create multi-agent system", "orchestrate agents", or "coordinate agent workflows".
Design and implement multi-agent systems using AI SDK v5 with structured handoffs, intelligent routing, and coordinated workflows across AI providers. This skill covers agent role definition, tool scoping, inter-agent delegation via handoff rules, and workflow orchestration patterns including coordinator-worker and supervisor topologies.
Prerequisites
Node.js 18+ and TypeScript 5.0+ runtime
AI SDK v5 (npm install ai @ai-sdk/openai @ai-sdk/anthropic @ai-sdk/google)
API keys for target providers set in environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_GENERATIVE_AI_API_KEY)
Zod for input/output schema validation (npm install zod)
Familiarity with agent-based architecture patterns (coordinator, pipeline, broadcast)
Instructions
Initialize a TypeScript project with tsconfig.json targeting ES2022 and moduleResolution
bundler
Install AI SDK v5 core and provider packages for each model backend required
Define agent roles by creating separate modules per agent, each with a system prompt, model binding, and scoped tool set
Implement tool functions using ai.tool() with Zod input/output schemas for type-safe execution
Configure handoff rules using ai.handoff() to delegate tasks between agents with clear trigger conditions and context passing
Build routing logic that classifies incoming requests by topic or intent and dispatches to the appropriate specialist agent
Wire agents into a workflow using sequential, parallel, or conditional orchestration patterns
Add state management to persist context across multi-step workflows using a shared context object or external store
Implement circuit breakers and timeout guards to prevent workflow deadlocks
Test each agent in isolation, then validate end-to-end handoff chains with representative inputs
See ${CLAUDE_SKILL_DIR}/references/implementation.md for the detailed implementation guide.
Output
TypeScript agent modules with AI SDK v5 provider bindings and system prompts
Tool definitions with Zod-validated input/output schemas
Workflow orchestration files defining sequential, parallel, and conditional execution paths
Routing classifier that maps user intents to specialist agents
Integration test suite covering handoff chains and fallback paths
Error Handling
Error
Cause
Solution
Provider configuration invalid
Missing or malformed API key in environment
Verify process.env.*_API_KEY values; check provider SDK version compatibility
Circular handoff detected
Agent A hands off to B which hands back to A
Implement handoff depth counter; set maxHandoffDepth and add a fallback terminal agent
Task routed to no agent
Routing classifier returned no match for input
Add a default catch-all route; improve classifier training data or keyword coverage
Tool access violation
Agent invoked a tool outside its scoped permission set
Review tools array per agent; ensure tool names match registered definitions exactly
Workflow timeout
Multi-step workflow exceeded deadline without completion
Set per-step timeouts with AbortController; add workflow-level deadline and partial-result handling
See ${CLAUDE_SKILL_DIR}/references/errors.md for the full error reference.
Examples
Scenario 1: Customer Support Triage -- A coordinator agent classifies incoming tickets as billing, technical, or general. Billing queries hand off to a specialist agent with access to Stripe tools. Technical queries route to a code-analysis agent with filesystem read tools. Resolution rate target: 85% automated within 3 handoff steps.
Scenario 2: Research Pipeline -- A sequential workflow chains a web-search agent, a summarization agent, and a report-writer agent. Each agent produces structured JSON output consumed by the next. The pipeline processes 50 research queries per batch with a p95 latency under 30 seconds per query.
Scenario 3: Code Review Multi-Agent -- A supervisor agent distributes pull request diffs to specialized reviewers (security, performance, style). Each reviewer returns findings with severity scores. The supervisor aggregates results into a unified review with prioritized action items.
See ${CLAUDE_SKILL_DIR}/references/examples.md for additional examples.
