Agent Design Principles
The best AI agents follow common design principles that make them effective, reliable, and maintainable:
- Single Responsibility: Each agent should do one thing well
- Clear Boundaries: Define exactly what the agent can and cannot do
- Graceful Degradation: Plan for edge cases and failures
- Human-in-the-Loop: Know when to escalate to humans
Defining Clear Objectives
Start every agent with a clear problem statement:
- What specific task should this agent accomplish?
- What does success look like?
- Who are the users and what do they need?
- What are the constraints and limitations?
Good vs. Bad Objectives
Bad: "Help with customer service"
Good: "Answer common product questions using our knowledge base, escalating complex issues to human agents within 2 minutes"
Configuring Permissions
Follow the principle of least privilege:
- Grant only the data access required for the task
- Enable only necessary integrations
- Require approval for sensitive actions
- Set appropriate rate limits
Testing and Iteration
Before Launch
- Test with diverse inputs covering expected use cases
- Verify edge case handling
- Confirm permission boundaries work correctly
- Review sample outputs for quality
Continuous Improvement
- Monitor real-world performance
- Collect user feedback
- Analyze failure cases
- Iterate based on data
Deployment Strategies
- Soft Launch: Start with a small user group
- Gradual Rollout: Expand based on success metrics
- Canary Deployment: Run new versions alongside old ones
Optimization Tips
- Keep prompts concise and specific
- Use structured outputs for consistency
- Cache common queries for speed
- Monitor token usage for cost control