---
# System prepended metadata

title: 'Fractal Agents: an amdhals law corollary '
tags: [ai, planning, agents]

---

# Fractal Structure for Parallel Agent Code Generation

https://v0-simulation-dashboard-design.vercel.app

> this dashboard is for visual, it most likely contains errors. Use it to get an approximate not percise scope


Parallel work is enabled by agentic operators, to enable and exploit this we specify first a directory structure to minimize concurrent access to files. We apply Amdahl's Law by maximizing the parallelizable fraction of work (p) and minimizing serial bottlenecks by deriving a corollary for designing the optimal file and directory structure

Amdahl's Law formula: 
$$ 
S_{\text{latency}} = \frac{1}{(1 - p) + \frac{p}{s}}
$$ 
Where:
- \( p \) = parallelizable fraction of work
- \( s \) = speedup factor for the parallel portion (e.g., number of developers adjusted for overhead)


Serial tasks require coordination, which is where human in the loop is best suited for. 

## Corollary for Optimal Structure Design

**Amdahl's Law for Code Generation:** The overall speedup in code generation is limited by the serial portion of the work. Therefore, the optimal structure should minimize serial bottlenecks (e.g., knowledge silos, high coupling) and maximize parallelizability (p) by:
- Ensuring that modules or components are highly cohesive and loosely coupled.
- Organizing code in a self-similar fractal pattern, where each unit (component, module, or state) can be developed independently by separate agents.
- Reducing the need for communication or coordination between agents during code generation.

**Fractal Structure as an Enabler:** The Fractal pattern naturally increases p by:
- **Localizing Changes:** Each component and its sub-components are contained within a dedicated directory, allowing agents to work on isolated parts without affecting others.
- **Scalable State Management:** The state is organized in a nested fractal manner, mirroring the data structure, which localizes state updates and reduces conflicts.
- **Simplifying Onboarding:** The consistent structure makes it easier for agents to understand and generate code for any part of the codebase, reducing the learning curve and serial dependencies.

#### Step-by-Step Application to Parallel Agent Code Generation
1. **Define the Parallelizable Fraction (p):**
   - In code generation, p represents the proportion of work that can be done in parallel by multiple agents. This includes generating code for independent components, modules, or features.
   - With a Fractal structure, p is high because each fractal node (e.g., a component folder) can be assigned to a separate agent without overlap. For example, agents can simultaneously generate code for `Login.js`, `Form.js`, and `ForgotModal.js` within the `login` directory.

2. **Minimize the Serial Portion (1-p):**
   - The serial portion includes tasks that require coordination, such as integrating shared components, resolving merge conflicts, or managing global state.
   - The Fractal structure reduces this by:
     - **Encapsulating Dependencies:** Sub-components are nested within parent components, so agents working on a parent component need only coordinate within their fractal branch.
     - **Shared Components in `src/components`:** Common components (e.g., Button, Input) are centralized, but since they are stable and reusable, they don't require frequent changes, thus minimizing serial work.
     - **Fractal State:** State management is decentralized per module, so agents can update state locally without global coordination.

3. **Calculate Speedup Using Amdahl's Law:**
   - Assume `n` agents are generating code. With a traditional flat structure, p might be low due to high coupling and knowledge silos. For example, if p=0.6 and n=5, the speedup is:

$$
     S = \frac{1}{(1 - 0.6) + \frac{0.6}{5}} = \frac{1}{0.4 + 0.12} = \frac{1}{0.52} \approx 1.92
$$

   - With a Fractal structure, p increases due to better modularity. Suppose p=0.9 and n=5:

$$
     S = \frac{1}{(1 - 0.9) + \frac{0.9}{5}} = \frac{1}{0.1 + 0.18} = \frac{1}{0.28} \approx 3.57 
$$
   - This shows a significant speedup (86% improvement) by adopting the Fractal structure.

4. **Implementing for AI Agents:**
   - **Agent Assignment:** Assign each AI agent to a fractal node (e.g., a specific component directory). Agents can generate code independently for their assigned node and its children.
   - **Code Generation Rules:** Agents should follow fractal conventions:
     - Generate components in CamelCase.
     - Create a lowerCamelCase folder for sub-components.
     - For state management, create nested reducers matching the data structure.
   - **Reducing Coordination:** Since each fractal node is self-contained, agents require minimal communication. Only when generating shared components (in `src/components`) might light coordination be needed, but this is rare.

5. **Use Case: Generating a Login Feature**
   - **Without Fractal:** Agents might generate all login-related code in a single file or scattered files, leading to merge conflicts and knowledge silos. Serial portion is high.
   - **With Fractal:** 
     - Agent A generates `src/pages/Login.js`.
     - Agent B generates `src/pages/login/Form.js`.
     - Agent C generates `src/pages/login/ForgotModal.js`.
     - Agent D generates `src/modules/login/actions.js` and `reducer.js` (using ducks pattern).
   - All agents work in parallel, and since each file is in its own context, conflicts are minimized. p is high, so speedup is nearly linear with n.

## Rust

#### Adapted Principles for Rust
Self-Similarity: Each module should have a consistent structure, making the project familiar at any level.

No Circular Dependencies: Rust already enforces this at compile time.

Organic Growth: Start with a few files and split modules as needed, avoiding boilerplate.

Inaccessible Sub-Sub-Tree: Use pub visibility to control access; only expose necessary items from a module.

Shared Resources: Place common modules at the appropriate level in the hierarchy rather than in shared folders.

Current State: Refactor the file structure to match the current logic, without future-proofing.

#### Adapted Rules for Rust
Rule 1: File names use snake_case (not kebab-case) to align with Rust conventions.

Rule 2: Use mod.rs files for module declarations, as required by Rust's module system.

Rule 3: Each file is a "mini-library" with a clear purpose and named exports.

Rule 4: Split outgrown files into sub-directories with mod.rs files.

Rule 5: Use absolute imports (e.g., crate::app::state::app_state::AppState) within the crate to avoid refactoring issues.

Rule 6: Avoid shared folders; place common functionality in modules at the closest common ancestor level.

Rule 7: Multiple entry points are supported via src/bin for binaries or examples.

Rule 8: Colocate unit tests in the same file using #[cfg(test)] mod tests.

Rule 9: Exceptions are allowed but should be minimized.

## Citations

TODO

## Disclaimer

AI aisstsnce in writing, not formulating or math related. My errors are of my own volitipn.

Including spelling. 