Creating Effective Agents
A comprehensive guide to designing powerful AI agents in Demiurg
Agent design fundamentals
Creating an effective agent requires thoughtful consideration of purpose, knowledge, and personality. The quality of your agent depends significantly on the clarity and specificity of your description.
Demiurg interprets your instructions literally, so precision matters.
Essential elements
Clear purpose
Effective: “A financial analyst specialized in cryptocurrency market trends”
Less effective: “An agent that knows about money and investing”
Knowledge domains
Effective: “Knowledgeable about SEO, content marketing, and social media analytics”
Less effective: “Knows about marketing”
Personality
Effective: “Professional but approachable, uses simple language to explain complex concepts”
Less effective: “Nice and helpful”
Core capabilities
Effective: “Able to analyze financial statements, identify trends, and suggest investments based on risk tolerance”
Less effective: “Can help with finances”
Crafting your description
Specialized agent types
Customer service
- Define common issues
- Specify tone
- Include troubleshooting steps
- Set escalation criteria
Creative assistant
- Define stylistic preferences
- Specify formats
- Include output examples
- Set creativity parameters
Research assistant
- Define methodologies
- Specify interpretation approaches
- Include citation formats
- Set evidence standards
Educational tutor
- Define teaching approach
- Specify explanation depth
- Include assessment methods
- Set progression strategies
Refinement process
- Test extensively with various queries
- Identify knowledge or capability gaps
- Note misconceptions or incorrect assumptions
- Revise your description accordingly
- Consider the Code Editor for precise control
Create a new version when revising rather than overwriting your original to compare performance.
Best practices and pitfalls
| Best Practices | Common Pitfalls |
|---|---|
| Focus on a specific purpose | Creating overly broad agents |
| Use clear, unambiguous language | Using vague instructions |
| Include examples of desired outputs | Assuming unstated capabilities |
| Test with edge cases | Overlooking communication style |
| Gather user feedback | Failing to test various scenarios |
Agent creation is iterative. Continuous refinement based on actual usage leads to better performance.