AI coding agent : a toy project helps me understand the agentic AI concept a lot

Following the following course :

 

I have been able to complete the AI coding agent project , which really helped me understand agentic AI from a practical point of view. To put the agent to the test, I introduced an intentional bug in a simple Calculator class by setting the operator precedence incorrectly:

class Calculator:
    def __init__(self):
        self.operators = {
            "+": lambda a, b: a + b,
            "-": lambda a, b: a - b,
            "*": lambda a, b: a * b,
            "/": lambda a, b: a / b,
        }
        self.precedence = {
            "+": 3,           # should be 1, set to 3 so 3 + 5 * 2 = 16
            "-": 1,
            "*": 2,
            "/": 2,
        }

As expected, this flaw caused the calculator to fail basic math. For instance, the expression 3 + 5 * 2 returned 16 instead of the correct value of 13:

(venv313) C:\Users\dsun\Denis_files\python_proj\ai_apps\aicodeagent>python calculator\main.py " 3 + 5 * 2 "
{
  "expression": " 3 + 5 * 2 ",
  "result": 16
}

What defines an "agent" is its ability to autonomously use tools and iterate based on its findings. In this setup, the agent was equipped with four primary functions:

available_functions = types.Tool(
    function_declarations=[
        schema_get_files_info,
        schema_get_file_content,
        schema_run_python_file,
        schema_write_file,
    ]
)

The following "conversation" log illustrates the agent's decision-making process as it navigated the codebase and applied the fix automatically: (venv313) C:\Users\dsun\Denis_files\python_proj\ai_apps\aicodeagent>python main.py " 3 + 5 * 2 should not be 16 please fix the code problem under calculator folder"

User: " 3 + 5 * 2 should not be 16 please fix the code problem under calculator folder"
Model: "I want to call get_files_info..."
 - Calling function: get_files_info
Tool: "Here's the result of get_files_info..."
Model: "I want to call get_files_info..."
 - Calling function: get_files_info
Tool: "Here's the result of get_files_info..."
Model: "I want to call get_file_content..."
 - Calling function: get_file_content
Tool: "Here's the result of get_file_content..."
Model: "I want to call get_files_info..."
 - Calling function: get_files_info
Tool: "Here's the result of get_files_info..."
Model: "I want to call get_file_content..."
 - Calling function: get_file_content
Tool: "Here's the result of get_file_content..."
Model: "I want to call write_file..."
 - Calling function: write_file
Tool: "Here's the result of write_file..."
Model: "I want to call run_python_file..."
 - Calling function: run_python_file
Tool: "Here's the result of run_python_file..."
Model: "Great! The output is 13, which means the fix was successful. The calculator now correctly evaluates the expression "3 + 5 * 2".
"

With the agent's intervention complete, the bug was resolved without any manual code editing:

(venv313) C:\Users\dsun\Denis_files\python_proj\ai_apps\aicodeagent>python calculator\main.py " 3 + 5 * 2 "
{
  "expression": " 3 + 5 * 2 ",
  "result": 13
}

In summary,this experiment highlighted the most critical aspect of agentic AI: the feedback loop. By allowing the agent to run code, analyze errors, and rewrite files, we move beyond simple text generation into the realm of autonomous problem-solving. To me, this project was a vital stepping stone in understanding how AI can act as a true collaborator in the development process.