We have seen how "Algorithmation" allows enterprises to package their rigid business rules (BPMN/DMN) into .uapf files. The promise is simple: Deterministic Logic + Creative AI = Safe Autonomy.

While the original examples focus on generic MCP hosts, today we will demonstrate how to implement this architecture using Google Gemini and its native Function Calling capabilities.

The Architecture

We are building a "Credit Approval Assistant."

The Brain: Google Gemini (providing the conversational interface).

The Law: A .uapf package containing the strict credit rules (BPMN/DMN).

The Middleware: A Python script that bridges Gemini's function calls to the Algomation Engine.

Step 1: The "Algorithmated" Service

Imagine we have a standard endpoint running our .uapf credit logic.

Endpoint: POST /uapf/execute-process

Input: { "salary": 5000, "debt": 100, "age": 30 }

Output: { "approved": true, "limit": 15000 }

Step 2: Defining the Tools for Gemini

Gemini doesn't guess; we give it tools. We define a function declaration that matches our UAPF logic.

Python

import google.generativeai as genai
from google.generativeai.types import FunctionDeclaration, Tool

# 1. Define the tool that represents our UAPF logic
credit_tool_schema = {
    "name": "run_credit_approval",
    "description": "Executes the official algorithmated credit approval process. Use this to determine eligibility and credit limits.",
    "parameters": {
        "type": "object",
        "properties": {
            "monthly_income": {"type": "integer", "description": "Applicant's monthly income in USD"},
            "monthly_debt": {"type": "integer", "description": "Applicant's total monthly debt payments"},
            "credit_score": {"type": "integer", "description": "FICO credit score"}
        },
        "required": ["monthly_income", "monthly_debt", "credit_score"]
    }
}

# 2. Configure the Gemini Model with this tool
model = genai.GenerativeModel(
    model_name='gemini-1.5-pro',
    tools=[Tool(function_declarations=[credit_tool_schema])]
)

chat = model.start_chat(enable_automatic_function_calling=True)

Step 3: The "Mock" Engine Connection

In a real scenario, the function below would send an HTTP request to the Algomation Engine (running the .uapf file). Here, we simulate the deterministic engine response.

Python

# The actual function code that runs when Gemini calls the tool
def run_credit_approval(monthly_income, monthly_debt, credit_score):
    # API Call to Algomation Engine would go here
    # response = requests.post("http://api.algomation.io/execute", json=...)
    
    # Simulating the Deterministic Engine Response
    print(f"\n[System] UAPF Engine invoked with score: {credit_score}...")
    
    if credit_score < 650:
        return {"decision": "DECLINED", "reason": "Policy DMN-01: Score below threshold"}
    
    dti = monthly_debt / monthly_income
    if dti > 0.40:
        return {"decision": "REFERRED", "reason": "Policy BPMN-02: High Debt-to-Income"}
        
    return {
        "decision": "APPROVED", 
        "limit": monthly_income * 3, 
        "audit_trace": "TraceID-998877"
    }

# Bind the function to the chat session
chat._tools.functions['run_credit_approval'] = run_credit_approval

Step 4: The Execution

Now, observe how Gemini relies on the tool rather than hallucinating an answer.

User Prompt:

"I make $5,000 a month, have $200 in debt payments, and my credit score is 720. Can I get a loan?"

Gemini's Internal Thought Process:

I need to answer a credit question.

I should not guess.

I have a tool run_credit_approval.

I will extract parameters: income=5000, debt=200, score=720.

System Output:

Plaintext

[System] UAPF Engine invoked with score: 720...

Gemini's Final Response:

"Based on the official credit approval process, your application is APPROVED.

You have been pre-qualified for a credit limit of $15,000. (Audit Trace: TraceID-998877)"

Why This Matters

By using Gemini's Function Calling as the middleware (similar to MCP), we achieve:

  • Explainability: We know exactly why the loan was approved (TraceID).
  • Consistency: Gemini will never approve a 400 credit score "because it felt like it."
  • Agility: If you change the logic in the .uapf file, Gemini updates instantly without retraining.

This is the future of Enterprise AI: Fluid conversation on the front, rock-solid algorithms on the back.

If you also use Claude alongside Gemini, compare this with Algorithmating Enterprise Processes for Claude AI Agents via UAPF and MCP .

For a broader view on how UAPF scales from single workflows to an enterprise-wide algorithm layer, see From Single Processes to the Algorithmated Enterprise .