---
layout: default
title: "Agentic Coding"
---

# Agentic Coding: Humans Design, Agents code!

> If you are an AI agents involved in building LLM Systems, read this guide **VERY, VERY** carefully! This is the most important chapter in the entire document. Throughout development, you should always (1) start with a small and simple solution, (2) design at a high level (`docs/design.md`) before implementation, and (3) frequently ask humans for feedback and clarification.
{: .warning }

## Agentic Coding Steps

Agentic Coding should be a collaboration between Human System Design and Agent Implementation:

| Steps                  | Human      | AI        | Comment                                                                 |
|:-----------------------|:----------:|:---------:|:------------------------------------------------------------------------|
| 1. Requirements | ★★★ High  | ★☆☆ Low   | Humans understand the requirements and context.                    |
| 2. Flow          | ★★☆ Medium | ★★☆ Medium |  Humans specify the high-level design, and the AI fills in the details. |
| 3. Utilities   | ★★☆ Medium | ★★☆ Medium | Humans provide available external APIs and integrations, and the AI helps with implementation. |
| 4. Node          | ★☆☆ Low   | ★★★ High  | The AI helps design the node types and data handling based on the flow.          |
| 5. Implementation      | ★☆☆ Low   | ★★★ High  |  The AI implements the flow based on the design. |
| 6. Optimization        | ★★☆ Medium | ★★☆ Medium | Humans evaluate the results, and the AI helps optimize. |
| 7. Reliability         | ★☆☆ Low   | ★★★ High  |  The AI writes test cases and addresses corner cases.     |

1. **Requirements**: Clarify the requirements for your project, and evaluate whether an AI system is a good fit. 
    - Understand AI systems' strengths and limitations:
      - **Good for**: Routine tasks requiring common sense (filling forms, replying to emails)
      - **Good for**: Creative tasks with well-defined inputs (building slides, writing SQL)
      - **Not good for**: Ambiguous problems requiring complex decision-making (business strategy, startup planning)
    - **Keep It User-Centric:** Explain the "problem" from the user's perspective rather than just listing features.
    - **Balance complexity vs. impact**: Aim to deliver the highest value features with minimal complexity early.

2. **Flow Design**: Outline at a high level, describe how your AI system orchestrates nodes.
    - Identify applicable design patterns (e.g., [Map Reduce](./design_pattern/mapreduce.md), [Agent](./design_pattern/agent.md), [RAG](./design_pattern/rag.md)).
      - For each node in the flow, start with a high-level one-line description of what it does.
      - If using **Map Reduce**, specify how to map (what to split) and how to reduce (how to combine).
      - If using **Agent**, specify what are the inputs (context) and what are the possible actions.
      - If using **RAG**, specify what to embed, noting that there's usually both offline (indexing) and online (retrieval) workflows.
    - Outline the flow and draw it in a mermaid diagram. For example:
      ```mermaid
      flowchart LR
          start[Start] --> batch[Batch]
          batch --> check[Check]
          check -->|OK| process
          check -->|Error| fix[Fix]
          fix --> check
          
          subgraph process[Process]
            step1[Step 1] --> step2[Step 2]
          end
          
          process --> endNode[End]
      ```
    - > **If Humans can't specify the flow, AI Agents can't automate it!** Before building an LLM system, thoroughly understand the problem and potential solution by manually solving example inputs to develop intuition.  
      {: .best-practice }

3. **Utilities**: Based on the Flow Design, identify and implement necessary utility functions.
    - Think of your AI system as the brain. It needs a body—these *external utility functions*—to interact with the real world:
        <div align="center"><img src="https://github.com/the-pocket/.github/raw/main/assets/utility.png?raw=true" width="400"/></div>

        - Reading inputs (e.g., retrieving Slack messages, reading emails)
        - Writing outputs (e.g., generating reports, sending emails)
        - Using external tools (e.g., calling LLMs, searching the web)
        - **NOTE**: *LLM-based tasks* (e.g., summarizing text, analyzing sentiment) are **NOT** utility functions; rather, they are *core functions* internal in the AI system.
    - For each utility function, implement it and write a simple test.
    - Document their input/output, as well as why they are necessary. For example:
      - `name`: `get_embedding` (`utils/get_embedding.py`)
      - `input`: `str`
      - `output`: a vector of 3072 floats
      - `necessity`: Used by the second node to embed text
    - Example utility implementation:
      ```python
      # utils/call_llm.py
      from openai import OpenAI

      def call_llm(prompt):    
          client = OpenAI(api_key="YOUR_API_KEY_HERE")
          r = client.chat.completions.create(
              model="gpt-4o",
              messages=[{"role": "user", "content": prompt}]
          )
          return r.choices[0].message.content
          
      if __name__ == "__main__":
          prompt = "What is the meaning of life?"
          print(call_llm(prompt))
      ```
    - > **Sometimes, design Utilies before Flow:**  For example, for an LLM project to automate a legacy system, the bottleneck will likely be the available interface to that system. Start by designing the hardest utilities for interfacing, and then build the flow around them.
      {: .best-practice }

4. **Node Design**: Plan how each node will read and write data, and use utility functions.
   - One core design principle for PocketFlow is to use a [shared store](./core_abstraction/communication.md), so start with a shared store design:
      - For simple systems, use an in-memory dictionary.
      - For more complex systems or when persistence is required, use a database.
      - **Don't Repeat Yourself**: Use in-memory references or foreign keys.
      - Example shared store design:
        ```python
        shared = {
            "user": {
                "id": "user123",
                "context": {                # Another nested dict
                    "weather": {"temp": 72, "condition": "sunny"},
                    "location": "San Francisco"
                }
            },
            "results": {}                   # Empty dict to store outputs
        }
        ```
   - For each [Node](./core_abstraction/node.md), describe its type, how it reads and writes data, and which utility function it uses. Keep it specific but high-level without codes. For example:
     - `type`: Regular (or Batch, or Async)
     - `prep`: Read "text" from the shared store
     - `exec`: Call the embedding utility function
     - `post`: Write "embedding" to the shared store

5. **Implementation**: Implement the initial nodes and flows based on the design.
   - 🎉 If you've reached this step, humans have finished the design. Now *Agentic Coding* begins!
   - **"Keep it simple, stupid!"** Avoid complex features and full-scale type checking.
   - **FAIL FAST**! Avoid `try` logic so you can quickly identify any weak points in the system.
   - Add logging throughout the code to facilitate debugging.

7. **Optimization**:
   - **Use Intuition**: For a quick initial evaluation, human intuition is often a good start.
   - **Redesign Flow (Back to Step 3)**: Consider breaking down tasks further, introducing agentic decisions, or better managing input contexts.
   - If your flow design is already solid, move on to micro-optimizations:
     - **Prompt Engineering**: Use clear, specific instructions with examples to reduce ambiguity.
     - **In-Context Learning**: Provide robust examples for tasks that are difficult to specify with instructions alone.

   - > **You'll likely iterate a lot!** Expect to repeat Steps 3–6 hundreds of times.
     >
     > <div align="center"><img src="https://github.com/the-pocket/.github/raw/main/assets/success.png?raw=true" width="400"/></div>
     {: .best-practice }

8. **Reliability**  
   - **Node Retries**: Add checks in the node `exec` to ensure outputs meet requirements, and consider increasing `max_retries` and `wait` times.
   - **Logging and Visualization**: Maintain logs of all attempts and visualize node results for easier debugging.
   - **Self-Evaluation**: Add a separate node (powered by an LLM) to review outputs when results are uncertain.

## Example LLM Project File Structure

```
my_project/
├── main.py
├── nodes.py
├── flow.py
├── utils/
│   ├── __init__.py
│   ├── call_llm.py
│   └── search_web.py
├── requirements.txt
└── docs/
    └── design.md
```

- **`docs/design.md`**: Contains project documentation for each step above. This should be *high-level* and *no-code*.
- **`utils/`**: Contains all utility functions.
  - It's recommended to dedicate one Python file to each API call, for example `call_llm.py` or `search_web.py`.
  - Each file should also include a `main()` function to try that API call
- **`nodes.py`**: Contains all the node definitions.
  ```python
  # nodes.py
  from pocketflow import Node
  from utils.call_llm import call_llm

  class GetQuestionNode(Node):
      def exec(self, _):
          # Get question directly from user input
          user_question = input("Enter your question: ")
          return user_question
      
      def post(self, shared, prep_res, exec_res):
          # Store the user's question
          shared["question"] = exec_res
          return "default"  # Go to the next node

  class AnswerNode(Node):
      def prep(self, shared):
          # Read question from shared
          return shared["question"]
      
      def exec(self, question):
          # Call LLM to get the answer
          return call_llm(question)
      
      def post(self, shared, prep_res, exec_res):
          # Store the answer in shared
          shared["answer"] = exec_res
  ```
- **`flow.py`**: Implements functions that create flows by importing node definitions and connecting them.
  ```python
  # flow.py
  from pocketflow import Flow
  from nodes import GetQuestionNode, AnswerNode

  def create_qa_flow():
      """Create and return a question-answering flow."""
      # Create nodes
      get_question_node = GetQuestionNode()
      answer_node = AnswerNode()
      
      # Connect nodes in sequence
      get_question_node >> answer_node
      
      # Create flow starting with input node
      return Flow(start=get_question_node)
  ```
- **`main.py`**: Serves as the project's entry point.
  ```python
  # main.py
  from flow import create_qa_flow

  # Example main function
  # Please replace this with your own main function
  def main():
      shared = {
          "question": None,  # Will be populated by GetQuestionNode from user input
          "answer": None     # Will be populated by AnswerNode
      }

      # Create the flow and run it
      qa_flow = create_qa_flow()
      qa_flow.run(shared)
      print(f"Question: {shared['question']}")
      print(f"Answer: {shared['answer']}")

  if __name__ == "__main__":
      main()
  ```