agent-project-development

Project Development Methodology

Principles for identifying tasks suited to LLM processing, designing effective project architectures, and iterating rapidly using agent-assisted development. Applies whether building a batch processing pipeline, a multi-agent research system, or an interactive agent application.

When to Activate

• Starting a new project that might benefit from LLM processing
• Evaluating whether a task is well-suited for agents versus traditional code
• Designing the architecture for an LLM-powered application
• Planning a batch processing pipeline with structured outputs
• Choosing between single-agent and multi-agent approaches
• Estimating costs and timelines for LLM-heavy projects

Decision Tree: Task-Model Fit

Should you use LLM processing for this task?
+-- Does it require synthesis across sources? --> Likely YES
+-- Does it involve subjective judgment with rubrics? --> Likely YES
+-- Is natural language the desired output? --> Likely YES
+-- Is there tolerance for individual errors? --> Likely YES
+-- Is the domain knowledge in the model's training? --> Likely YES
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+-- Does it require precise computation? --> Likely NO (use traditional code)
+-- Does it need real-time sub-second responses? --> Likely NO
+-- Does it require perfect accuracy? --> Likely NO (hallucination risk)
+-- Does it depend on proprietary data the model lacks? --> Likely NO
+-- Must same input produce identical output? --> Likely NO
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+-- Mixed? --> Manual prototype first (5 minutes saves weeks)

Core Concepts

The Manual Prototype Step

Before investing in automation, validate task-model fit with a manual test. Copy one representative input into the model interface. Evaluate the output quality. This takes minutes and prevents hours of wasted development.

If the manual prototype fails, the automated system will fail. If it succeeds, you have a baseline and a template for prompt design.

Pipeline Architecture

LLM projects benefit from staged pipeline architectures where each stage is:

• Discrete: Clear boundaries between stages
• Idempotent: Re-running produces the same result
• Cacheable: Intermediate results persist to disk
• Independent: Each stage can run separately