Agent Workflow Patterns

Purpose: Provide production-ready agent architectures, workflow patterns, and loop control strategies for building autonomous AI systems with Vercel AI SDK.

Activation Triggers:

• Building autonomous AI agents

• Implementing multi-step reasoning

• Creating agent workflows

• Tool orchestration and coordination

• Loop control and iteration management

• Multi-agent system architectures

• ReAct (Reasoning + Acting) patterns

Key Resources:

• templates/react-agent.ts

• ReAct agent pattern

• templates/multi-agent-system.ts

• Multiple specialized agents

• templates/workflow-orchestrator.ts

• Workflow coordination

• templates/loop-control.ts

• Iteration and safeguards

• templates/tool-coordinator.ts

• Tool orchestration

• scripts/validate-agent.sh

• Validate agent configuration

• examples/

• Production agent implementations (RAG agent, SQL agent, etc.)

Core Agent Patterns

1. ReAct Agent (Reasoning + Acting)

When to use: Complex problem-solving requiring iterative thought and action

Template: templates/react-agent.ts

Pattern:

async function reactAgent(task: string, maxIterations: number = 5) { const tools = { /* tool definitions */ } let iteration = 0

while (iteration < maxIterations) { // Reasoning step const thought = await generateText({ model: openai('gpt-4o') messages: [ { role: 'system', content: 'Think step-by-step...' } { role: 'user', content: task } ] })

// Acting step (tool calls)
const action = await generateText({
  model: openai('gpt-4o')
  tools
  toolChoice: 'auto'
  messages: [/* ... */]
})

// Check if task complete
if (isComplete(action)) break
iteration++

}

return result }

Best for: Research, analysis, complex planning

2. Multi-Agent System

When to use: Complex domains requiring specialized expertise

Template: templates/multi-agent-system.ts

Pattern:

• Coordinator agent routes tasks

• Specialist agents handle specific domains

• Result aggregation and synthesis

Best for: Multi-domain problems, parallel task execution

3. Workflow Orchestration

When to use: Pre-defined sequences of steps

Template: templates/workflow-orchestrator.ts

Pattern:

• Define workflow steps

• Execute sequentially with error handling

• State management between steps

• Conditional branching

Best for: Structured processes, pipelines

Loop Control Strategies

1. Iteration Limits

const config = { maxIterations: 10 onMaxIterations: 'return-last' | 'throw-error' }

Prevents: Infinite loops

2. Cost Limits

const config = { maxTokens: 10000 onMaxTokens: 'graceful-stop' }

Prevents: Runaway costs

3. Time Limits

const config = { maxDuration: 30000, // 30 seconds onTimeout: 'return-partial' }

Prevents: Long-running operations

4. Quality Gates

const config = { stopCondition: (result) => result.confidence > 0.9 }

Ensures: Quality outputs

Tool Orchestration

Sequential Tool Execution

const tools = { search: tool({ /* ... / }) analyze: tool({ / ... / }) summarize: tool({ / ... */ }) }

// AI decides order and usage const result = await generateText({ model: openai('gpt-4o') tools maxToolRoundtrips: 5 })

Parallel Tool Execution

const results = await Promise.all([ callTool('search', { query: 'topic1' }) callTool('search', { query: 'topic2' }) callTool('search', { query: 'topic3' }) ])

Agent State Management

interface AgentState { conversation: Message[] context: Record<string, any> toolResults: ToolResult[] iteration: number }

class StatefulAgent { private state: AgentState

async execute(task: string) { while (!this.isComplete()) { await this.step() this.updateState() } return this.state } }

Production Best Practices

1. Error Recovery

try { result = await agent.execute(task) } catch (error) { if (error.code === 'MAX_ITERATIONS') { return agent.getBestSoFar() } throw error }

2. Monitoring

agent.on('iteration', ({ count, result }) => { metrics.record('agent.iteration', { count }) })

3. Safeguards

• Rate limiting

• Input validation

• Output sanitization

• Cost tracking

Common Agent Architectures

1. RAG Agent

Example: examples/rag-agent.ts

Retrieves information and answers questions

2. SQL Agent

Example: examples/sql-agent.ts

Queries databases using natural language

3. Research Agent

Example: examples/research-agent.ts

Gathers and synthesizes information

4. Code Agent

Example: examples/code-agent.ts

Writes and debugs code

Resources

Templates:

• react-agent.ts

• ReAct pattern implementation

• multi-agent-system.ts

• Multi-agent coordination

• workflow-orchestrator.ts

• Workflow execution

• loop-control.ts

• Iteration safeguards

• tool-coordinator.ts

• Tool orchestration

Scripts:

• validate-agent.sh
• Agent config validation

Examples:

• rag-agent.ts

• Complete RAG agent

• sql-agent.ts

• Natural language SQL

• research-agent.ts

• Information gathering

• code-agent.ts

• Code generation

SDK Version: Vercel AI SDK 5+ Agent Frameworks: Built-in tools, MCP integration

Best Practice: Start simple (single tool), add complexity as needed