3.4.1b Basic tool with AI demo
← 3.4 Agentic TF semantic demos
1b Basic tool (with AI) demo
This is actually the first demo where TF/UFA semantic capabilities directly enable external deterministic execution.
1b.1 py script
# ai_demo_01b_tool_openai.py
# Basic OpenAI tool-calling style demo
import json
import os
from dotenv import load_dotenv
from openai import OpenAI
# -----------------------------------
# Load API key
# -----------------------------------
load_dotenv()
client = OpenAI(
api_key=os.getenv("OPENAI_API_KEY")
)
# -----------------------------------
# Tool implementation
# -----------------------------------
def calculator(operation, a, b):
if operation == "add":
return a + b
if operation == "subtract":
return a - b
if operation == "multiply":
return a * b
if operation == "divide":
return a / b
raise ValueError(f"Unknown operation: {operation}")
# -----------------------------------
# Prompt
# -----------------------------------
user_prompt = "What is 11 multiplied by 6?"
system_prompt = """
You are an AI agent.
Return ONLY JSON.
Valid format:
{
"tool": "calculator",
"operation": "multiply",
"a": 12,
"b": 7
}
"""
# -----------------------------------
# Call OpenAI (REAL LLM OUTPUT)
# -----------------------------------
response = client.chat.completions.create(
model="gpt-4.1-mini",
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=0
)
llm_output = response.choices[0].message.content
print("\nLLM OUTPUT:")
print(llm_output)
# -----------------------------------
# Parse JSON
# -----------------------------------
action = json.loads(llm_output)
# -----------------------------------
# External agent executes tool
# -----------------------------------
if action["tool"] == "calculator":
result = calculator(
action["operation"],
action["a"],
action["b"]
)
final_result = {
"tool": action["tool"],
"operation": action["operation"],
"result": result
}
print("\nCODE EXECUTED:")
print(json.dumps(final_result, indent=2))
1b.2 Test
system_prompt = """
You are an AI agent.
Return ONLY JSON.
Valid format:
{
"tool": "calculator",
"operation": "multiply",
"a": 12,
"b": 7
}
"""
user_prompt = “What is 11 multiplied by 6?”
python ai_demo_01b_tool_openai.py
LLM OUTPUT:
{
"tool": "calculator",
"operation": "multiply",
"a": 11,
"b": 6
}
CODE EXECUTED:
{
"tool": "calculator",
"operation": "multiply",
"result": 66
}
user_prompt = “What is 5 plus 3?”
python ai_demo_01b_tool_openai.py
LLM OUTPUT:
{
"tool": "calculator",
"operation": "add",
"a": 5,
"b": 3
}
CODE EXECUTED:
{
"tool": "calculator",
"operation": "add",
"result": 8
}
1b.3 MOST important TF/UFA capabilities are:
@8 Semantic interpretation / inference
The TF must understand: “What is 12 multiplied by 7?” That is semantic interpretation.
But it also understood “What is 5 plus 3?” with the same system prompt.
@7 Structured constrained output generation
The TF must generate: { “tool”: “calculator”, “operation”: “multiply”, “a”: 12, “b”: 7 } in valid machine-readable structure. That is constrained structured output generation.
1b.4 Weak/minor involvement
@1 Generalization
Minor involvement. The TF can handle: “What is 12 times 7?” “What is 12 multiplied by 7?” “Compute 12 x 7” without exact training matches.
@4 Human-language robustness
Minor involvement. Could tolerate: “wat is 12 tiemz 7”
1b.5 NOT really involved
These are mostly NOT important in Step 1:
- @2 contextual tracking
- @3 feature abstraction
- @5 ontology alignment
- @6 explanation synthesis
- @9 planning/workflow synthesis
because the demo is intentionally simple.
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