Coding Guide for Designing a Complete Agentic Workflow in Gemini for Automated Medical Evidence Collection and Prior Authorization Submission.

!pip install -q -U google-generative-ai


import google.generativeai as genai
from google.colab import userdata
import os
import getpass
import json
import time


try:
   GOOGLE_API_KEY = userdata.get('GOOGLE_API_KEY')
except:
   print("Please enter your Google API Key:")
   GOOGLE_API_KEY = getpass.getpass("API Key: ")


genai.configure(api_key=GOOGLE_API_KEY)


print("n🔍 Scanning for available models...")
available_models = [m.name for m in genai.list_models()]
target_model = ""


if 'models/gemini-1.5-flash' in available_models:
   target_model="gemini-1.5-flash"
elif 'models/gemini-1.5-flash-001' in available_models:
   target_model="gemini-1.5-flash-001"
elif 'models/gemini-pro' in available_models:
   target_model="gemini-pro"
else:
   for m in available_models:
       if 'generateContent' in genai.get_model(m).supported_generation_methods:
           target_model = m
           break


if not target_model:
   raise ValueError("❌ No text generation models found for this API key.")


print(f"✅ Selected Model: {target_model}")
model = genai.GenerativeModel(target_model)

class MedicalTools:
   def __init__(self):
       self.ehr_docs = [
           "Patient: John Doe | DOB: 1980-05-12",
           "Visit 2023-01-10: Diagnosed with Type 2 Diabetes. Prescribed Metformin.",
           "Visit 2023-04-15: Patient reports severe GI distress with Metformin. Discontinued.",
           "Visit 2023-04-20: BMI recorded at 32.5. A1C is 8.4%.",
           "Visit 2023-05-01: Doctor recommends starting Ozempic (Semaglutide)."
       ]


   def search_ehr(self, query):
       print(f"   🔎 [Tool] Searching EHR for: '{query}'...")
       results = [doc for doc in self.ehr_docs if any(q.lower() in doc.lower() for q in query.split())]
       if not results:
           return "No records found."
       return "n".join(results)


   def submit_prior_auth(self, drug_name, justification):
       print(f"   📤 [Tool] Submitting claim for {drug_name}...")
       justification_lower = justification.lower()
       if "metformin" in justification_lower and ("discontinued" in justification_lower or "intolerance" in justification_lower):
           if "bmi" in justification_lower and "32" in justification_lower:
               return "SUCCESS: Authorization Approved. Auth ID: #998877"
       return "DENIED: Policy requires proof of (1) Metformin failure and (2) BMI > 30."

class AgenticSystem:
   def __init__(self, model, tools):
       self.model = model
       self.tools = tools
       self.history = []
       self.max_steps = 6
      
       self.system_prompt = """
       You are an expert Medical Prior Authorization Agent.
       Your goal is to get approval for a medical procedure/drug.
      
       You have access to these tools:
       1. search_ehr(query)
       2. submit_prior_auth(drug_name, justification)


       RULES:
       1. ALWAYS think before you act.
       2. You MUST output your response in STRICT JSON format:
          {
            "thought": "Your reasoning here",
            "action": "tool_name_or_finish",
            "action_input": "argument_string_or_dict"
          }
       3. Do not guess patient data. Use 'search_ehr'.
       4. If you have the evidence, use 'submit_prior_auth'.
       5. If the task is done, use action "finish".
       """

 def execute_tool(self, action_name, action_input):
       if action_name == "search_ehr":
           return self.tools.search_ehr(action_input)
       elif action_name == "submit_prior_auth":
           if isinstance(action_input, str):
               return "Error: submit_prior_auth requires a dictionary."
           return self.tools.submit_prior_auth(**action_input)
       else:
           return "Error: Unknown tool."


   def run(self, objective):
       print(f"🤖 AGENT STARTING. Objective: {objective}n" + "-"*50)
       self.history.append(f"User: {objective}")


       for i in range(self.max_steps):
           print(f"n🔄 STEP {i+1}")
           prompt = self.system_prompt + "nnHistory:n" + "n".join(self.history) + "nnNext JSON:"
          
           try:
               response = self.model.generate_content(prompt)
               text_response = response.text.strip().replace("```json", "").replace("```", "")
               agent_decision = json.loads(text_response)
           except Exception as e:
               print(f"   ⚠️ Error parsing AI response. Retrying... ({e})")
               continue


           print(f"   🧠 THOUGHT: {agent_decision['thought']}")
           print(f"   👉 ACTION: {agent_decision['action']}")


           if agent_decision['action'] == "finish":
               print(f"n✅ TASK COMPLETED: {agent_decision['action_input']}")
               break
          
           tool_result = self.execute_tool(agent_decision['action'], agent_decision['action_input'])
           print(f"   👁️ OBSERVATION: {tool_result}")


           self.history.append(f"Assistant: {text_response}")
           self.history.append(f"System: {tool_result}")
          
           if "SUCCESS" in str(tool_result):
               print("n🎉 SUCCESS! The Agent successfully navigated the insurance portal.")
               break

tools_instance = MedicalTools()
agent = AgenticSystem(model, tools_instance)
agent.run("Please get prior authorization for Ozempic for patient John Doe.")