Best Practices for Building with Memex
Master the art of "agent engineering"—the skill of getting the most out of your AI development partner.
Understanding Agent Engineering
Just like "prompt engineering" helps you get better results from language models, "agent engineering" is a set of best practices to maximize your success with Memex. While Memex is powerful and intuitive, following these practices will significantly improve your outcomes, especially for complex projects.
Agent engineering is about designing thoughtful interactions with AI systems to ensure they operate reliably, efficiently, and successfully. Even as AI models become more sophisticated, these practices help guide them toward the results you want.
The Five Pillars of Successful Building
1. Separate Planning from Execution
Before diving into code, make sure you have clear requirements. If your project needs are still forming, start with collaborative planning:
I'm not sure about all the requirements for my project. I want to build a data dashboard for my team, but I need help working through the details. Don't start building until I give you the go-ahead.Use Plan Mode to work through requirements systematically. This mode acts like an experienced technical product manager, asking the right questions and helping you scope appropriately.
2. Build Incrementally
The most common mistake is asking for too much at once. Break your project into small, achievable steps:
Instead of this:
Try this approach:
Then continue step by step:
This incremental approach has multiple benefits:
Higher success rate for each step
Easier to debug when issues arise
Natural checkpoints to save progress
Flexibility to change direction based on results
3. Verify Understanding Before Making Changes
When working with existing code, always ensure Memex understands the current system before asking for modifications.
The wrong approach:
The right approach:
This pattern is crucial for success with existing codebases. Just like human developers study code before modifying it, Memex needs guided exploration to understand how things work.
4. Manage Task Execution
Some tasks require multiple iterations. Instead of micromanaging every step, you can give Memex more autonomy:
For ongoing work:
For quick iterations:
When things get complex:
5. Handle Errors Effectively
When Memex gets stuck or makes mistakes, use these recovery strategies:
For spinning wheels or repeated failures:
For dependency or environment issues:
For complex debugging:
Common Success Patterns
a. The MVP-First Pattern
Always start with the minimum viable version:
Core functionality only
Add one feature at a time
Enhance the user experience
b. The Context Pattern
For projects you'll return to later:
This helps with:
Context for future conversations
Onboarding team members
Maintaining project knowledge
c. The Version Control Pattern
Establish good practices early:
Regular commits create safety nets and clear progress markers.
d. The Testing Pattern
Build confidence in your code:
Debugging and Recovery Strategies
When Memex Says It Can't Do Something
"I need sudo privileges":
"I can't execute code on your machine":
"I don't have access to the latest API documentation":
When Data or Dependencies Are Missing
Missing datasets:
Dependency conflicts:
Interactive CLI tools:
When Projects Get Complex
Lost in complexity:
Performance or design issues:
Advanced Techniques
Context Management for Large Projects
As projects grow, help Memex maintain context:
Multi-Conversation Projects
When starting new conversations in existing projects:
Custom Instructions for Consistency
Set project-specific guidelines in your Custom Instructions:
Common Anti-Patterns to Avoid
1. The "Do Everything" Anti-Pattern
Don't ask for complex, multi-part applications in a single request. Break them down.
2. The "Too Specific" Anti-Pattern
Avoid being overly prescriptive about implementation details unless you have specific constraints.
3. The "No Verification" Anti-Pattern
Always ask Memex to test and verify its work rather than checking everything manually.
4. The "Context Overload" Anti-Pattern
Don't dump massive amounts of context at once. Introduce complexity gradually.
5. The "No Progress Saves" Anti-Pattern
Don't forget to commit working versions. Use git to create checkpoints at logical milestones.
Measuring Success
Good agent engineering leads to:
Higher success rates for complex tasks
Fewer iterations needed to achieve goals
More maintainable project outcomes
Better project continuity across conversations
Reduced frustration with debugging and errors
Community and Learning
Share your own patterns in our Discord community
Learn from others' approaches and contribute your discoveries
Remember: agent engineering is a skill that improves with practice. Start with these patterns, experiment with your own approaches, and don't hesitate to ask the community for advice when you encounter new challenges.
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