Task Decomposition
Breaking complex tasks into smaller, manageable subtasks to overcome limitations like context window constraints in AI systems.
Key Principles
- Atomicity: Subtasks must be small enough to complete within context window limits (e.g., <1k tokens for AI agents).
- Sequential Dependencies: Subtasks ordered to build incrementally (output of one informs next).
- Context Minimization: Each subtask uses only necessary context from prior steps.
AI Coding Workflow (Claude Implementation)
- Problem: AI agents fail at “one-shot” large code generation due to context window limitations.
- Solution: Decompose coding tasks into iterative, context-aware subtasks.
- Workflow:
- Break feature into atomic steps (e.g., “parse input”, “validate data”, “generate output”).
- For each step:
- Generate code snippet.
- Validate against constraints.
- Pass result to next subtask.
Advanced Orchestration Examples
- Sakana Fugu Ultra: Demonstrates high-level orchestration of complex tasks via task decomposition.
- App Cloning: Converts screenshots into working applications by breaking down UX replication into discrete coding subtasks.
- Complex Problem Solving: Handles intricate logic by chaining specialized agents (e.g., Hermes Agent) to solve sub-problems sequentially.
- See: Fugu Ultra AI Orchestrates App Cloning and Complex Problem Solving