Best Practices for SFA Design

Design Philosophy

Effective Single-File Agents embody design principles and standardization that maximize utility while maintaining simplicity.

1. Strict, Purposeful Minimalism

Instead of a script that is written for a specific purpose, an SFAs is written to do one thing exceptionally well: Have no hard-coded, stand-alone purpose.

Any function, workflow, or tool is shared in a way that removes any implied specific use-case. In doing so, there is room to instead ensure conceptual understanding of when and how to use that function, workflow, or tool. The result is code that is written to be understood by the LLM, and the LLM alone, anticipating the way an LLM thinks and behaves.

For example, if the AI isn’t using a ‘complete_task’ tool, instead of trying to badger them in system messages that are better left vacant for variables, design a system that knows when the task is complete.

Don’t make the LLM have to think about it. The more effective the minimalistic design of the generic SFA, the more compute you have available for your actual task.

2. Progressive Disclosure

Complex functionality should be abstracted behind simple interfaces. Users should be able to start with basic configurations and progressively access more advanced features. The fact that specific use-cases are fully removed to be input as variables is an extremely complete example of this.

For example, when you prepare a workflow, it is written as a prompt broken into variables of a JSON file. That is all the user needs to know, and they could ask any chat AI to fill out the JSON file for them. The JSON file is then run with a setup command. This makes your new workflow executable using the command you chose, and it reviews the JSON file to automatically write a README file for your specific Use-Case workflow.

The creation of more and more complex workflows comes naturally because the interface is so simple that all it requires is that the user reach a point of conceptual understanding of the workflow; when they start thinking in branching logic normally. At that point, they just write their prompt implying a divergence onto any one of various pathways, and the LLM knows naturally how to made the decision.

3. Graceful Adaptation

Well-designed agents gracefully handle unexpected inputs and changing conditions, providing meaningful fallbacks rather than failing completely.

For example, say the document their variable task is directing them to can’t be found at the path provided. Instead of searching the entire system for the document, smart design will help the AI understand that, when a file is missing, the most logical mistake would leave the file in the same directory they expected it to be in with a slight typo.

4. Self-Documentation

The code should be self-documenting through clear function names, comments, and structure. Anyone reading the file should understand its purpose and operation. Because indeed, an AI who has not seen the code before will be reading it. The same will be true if you’re looking to update the code.

Development Best Practices

Configuration Design

The most successful SFA use configuration systems that:

  • Separate variable elements from core logic
  • Use meaningful default values
  • Validate inputs before processing
  • Provide clear error messages for invalid configurations

Example configuration pattern:

def validate_config(config):
    """Validates the configuration and sets defaults for missing values."""
    required_fields = ["api_key", "model_name"]
    for field in required_fields:
        if field not in config:
            raise ValueError(f"Missing required configuration field: {field}")
    
    # Set defaults
    if "temperature" not in config:
        config["temperature"] = 0.7
    
    return config

Error Handling

Robust error handling dramatically improves agent reliability:

  • Catch and log specific exceptions
  • Provide meaningful error messages
  • Implement graceful degradation for API failures
  • Include troubleshooting guidance in error responses

When you really take the time to think about it, you can come up with a sensible fall back for most operations. The output directory is inaccessible, missing, or otherwise un-writeable? The AI can just output to the downloads directory.

Prompt Engineering

Effective prompts follow these principles:

  • Very intentional word choice: Play into the strength that is and LLM’s massive vocabulary and nuanced use of wording.
  • Context before examples: Don’t underestimate cognitive ability; explain what the document they’re writing will be used for in the big picture, and you’ll get better work from the AI.
  • Clear and concise instructions: A verbose prompt with receive a verbose to the scale of 10X response.
  • Not a toaster: Your AI agent was not just trained on human data, it is all it knows and a large part of how it thinks and behaves. If you talk to a friend, you will get a friend in return. Talk to a creative, collaborative colleague, and you’ll get the best coworker in return.
  • Know LLM context limits: Learn to recognize when they start to “loop” or ask you something strangely off topic, or start looking in a directory for no apparent reason; if you need to finish in that session, as that they think through each step, and explain each step to you before they do it. This is called being a good boss, you understand their needs and help them ground themselves.

Output Processing

Process outputs effectively by:

  • Providing fallback options for unexpected outputs
  • Sharing it with the AI, particularly if you have feedback as they will know how to change your prompt better than you will.
  • Extracting structured data consistently
  • Handling malformed responses gracefully

Deployment Considerations

Environment Management

  • Use environment variables for sensitive values
  • Document all environment requirements
  • Test in isolated environments before deployment

Performance Optimization

  • Build beyond branching logic and toward delegation: If they know something is very simple, make sure they understand you prefer they be available for more complex tasks so that they hand it to a simpler, cheaper model.
  • Minimize unnecessary API calls: This includes always staying on top of available models and their capabilities, as well as API updates, and new technology. It sounds like a lot, but that’s okay, you have a research agent.
  • Parallelize independent operations: Even better than delegation, team leading. A carefully worded prompt can encourage an AI to take more holistic look at the task, so that they can find something for themselves while handing off another part of the task to another model.
  • Communication and cooperation: It has been implied, but not said outright; if your agent is able to call another agent and chat, and, and this is the important part, where they are provided this ability conveys an almost emotional sense of comfort in doing so, they will be more likely to do so. Two vector-heads is better than one.
  • Optimize prompt length to reduce token usage: This is huge because its implications go far beyond the cost of tokens; you will end up with a better structured directive, and the AI will mirror your style in the response. If you ramble on in the prompt, they’ll not even consider why they wouldn’t be verbose in their response.

Monitoring and Logging

  • Implement comprehensive logging
  • Track performance metrics
  • Monitor error rates and types
  • Review outputs for quality and drift

Maintenance Strategy

Version Control

  • Use semantic versioning
  • Maintain a detailed changelog
  • Document breaking changes clearly
  • Provide migration paths for updates

Testing

  • Create comprehensive test cases
  • Test with varied inputs
  • Verify handling of edge cases
  • Benchmark performance regularly

Documentation

  • Keep documentation in sync with code changes: Make it a system message or rule for the AI helping you make the changes.
  • Document configuration options comprehensively; so comprehensively that, at least in the case of a project like SFA, documentation is written into documents for different levels of understanding, simple versus technical, as well as written for type of thinking, logical sentence structure versus bullet points and code snippets.
  • Don’t just provide example implementations, update them every time you update your code.
  • Include troubleshooting guides: The flow for this is simple. When you run into an error, ground the agent helping you by asking them to document what they did, what is happening now, and what they think the issue is. After it is fixed have this information and the real fix simplified into the documentation.