Designing AI Workflows

Build robust and scalable AI workflows that integrate seamlessly with your application.

Workflow Architecture

An effective AI workflow consists of:

• Input Layer: User input and data collection
• Processing Layer: Agent logic and decision-making
• Tool Layer: External services and data access
• Output Layer: Response formatting and delivery
• Feedback Loop: Monitoring and improvement

Planning Your Workflow

Define Clear Objectives

Before building, determine:

• What problem does the workflow solve?
• Who are the end users?
• What constraints exist (latency, cost, accuracy)?
• How will success be measured?

Map User Interactions

Visualize the journey:

1. How does the user trigger the workflow?
2. What information do they provide?
3. What intermediate steps are needed?
4. How does the agent communicate progress?
5. What is the final outcome?

Error Handling and Recovery

Workflows must be resilient:

• Graceful Degradation: Provide partial results when full execution fails
• Retry Logic: Automatically retry failed operations
• Fallback Handlers: Use alternative approaches when primary fails
• User Notifications: Keep users informed of issues

Performance Optimization

Optimize workflow efficiency:

• Caching: Store frequently accessed data
• Parallel Execution: Run independent operations concurrently
• Streaming: Return partial results immediately
• Resource Management: Monitor and limit resource usage

State Management

Maintain workflow state effectively:

• Session State: Data for current workflow instance
• Agent State: Agent memory and context
• Tool State: External service state
• Cleanup: Proper resource cleanup after completion

Monitoring and Analytics

Track workflow performance:

• Execution Time: Measure workflow duration
• Success Rate: Track completion vs failures
• User Satisfaction: Collect feedback
• Cost Analysis: Monitor operational expenses

Example: Customer Support Workflow

A typical support workflow includes:

1. Initial inquiry classification by agent
2. Retrieval of relevant documentation
3. Generation of initial response
4. User feedback collection
5. Escalation if needed
6. Resolution confirmation

Next Steps

Learn specific techniques in Prompting Strategies and explore real-world implementations in Examples & Use Cases.