Agent Best Practices
Design Principles
1. Clear Purpose
Each agent should have a single, well-defined purpose:
✅ Good: "Extract invoice amounts and due dates"
❌ Bad: "Process documents"2. Appropriate Scope
- Keep agent logic focused
- Don't try to do too much
- Break complex tasks into multiple agents
3. Error Handling
Always plan for failures:
- Invalid input format
- API timeouts
- Unexpected data
- Rate limitingConfiguration
Input Validation
Always validate inputs:
1. Check format (JSON, PDF, etc.)
2. Verify required fields
3. Validate data types
4. Check file size/lengthOutput Formatting
Define clear output format:
✅ Consistent structure
✅ Documented field names
✅ Clear error messages
✅ Useful debugging infoTesting
Before Deployment
- Unit tests - Individual components
- Integration tests - With dependencies
- Smoke tests - Happy path
- Edge cases - Unusual inputs
Test Cases
python
# Test successful operation
test_valid_document()
# Test error handling
test_invalid_format()
test_missing_fields()
test_large_file()
# Test edge cases
test_empty_input()
test_special_characters()
test_concurrent_requests()Performance Optimization
1. Reduce Processing Time
- Cache frequently accessed data
- Optimize AI prompts
- Use batch operations
- Parallel process when possible
2. Monitor Usage
- Track execution time
- Monitor resource usage
- Alert on anomalies
- Analyze bottlenecks
3. Scale Effectively
- Start small, scale gradually
- Use load balancing
- Implement rate limiting
- Set quotas
Security
1. Data Protection
- Encrypt sensitive data
- Validate all inputs
- Sanitize outputs
- Log access
2. API Security
- Use API keys securely
- Implement rate limits
- Validate requests
- Monitor for abuse
3. Access Control
- Restrict to necessary users
- Use role-based access
- Audit all executions
- Regular security reviews
Monitoring & Debugging
1. Logging
Log important events:
python
logger.info(f"Agent started: {agent_id}")
logger.debug(f"Processing document: {doc_id}")
logger.error(f"Failed to process: {error}")2. Metrics
Track key metrics:
- Execution time
- Success rate
- Error rate
- Resource usage
3. Alerting
Alert on:
- High error rates
- Performance degradation
- Resource exhaustion
- Unusual patterns
Agent Orchestration
Sequential Workflow
Agent 1 → Agent 2 → Agent 3 → OutputUse when output of one agent is input to next.
Parallel Workflow
Agent 1 ─┐
├─→ Agent 3 → Output
Agent 2 ─┘Use for independent operations.
Conditional Workflow
Agent 1 → Decision → Agent 2 or 3 → OutputUse based on result of first agent.
Common Mistakes
❌ Don't
- Hardcode credentials
- Ignore errors
- Skip validation
- Process unbounded data
- Mix concerns
- Skip testing
- Ignore logging
✅ Do
- Use environment variables
- Handle errors gracefully
- Validate all inputs
- Set resource limits
- Separate concerns
- Test thoroughly
- Log everything
Documentation
For Each Agent
Document:
- Purpose and use case
- Input format and requirements
- Output format and structure
- Error codes and handling
- Performance characteristics
- Security considerations
- Example usage
- Troubleshooting guide
Version Management
Versioning Strategy
v1.0.0 - Initial release
v1.1.0 - Performance improvements
v1.2.0 - Bug fixes
v2.0.0 - Breaking changesMigration Guide
When making breaking changes:
- Document changes
- Provide migration path
- Support old version temporarily
- Clear deprecation message
Deployment Checklist
Before deploying agent:
- [ ] Code reviewed
- [ ] Tests passing
- [ ] Documentation updated
- [ ] Error handling verified
- [ ] Logging configured
- [ ] Security reviewed
- [ ] Performance tested
- [ ] Monitoring setup
- [ ] Runbook created
- [ ] Team notified