Agentic AI - Building Autonomous Systems

from langchain import LLMChain, PromptTemplate
from langchain.chat_models import ChatOpenAI

# Initialize the LLM
llm = ChatOpenAI(model="gpt-4", temperature=0.7)

# Step 1: Generate initial output
generator_prompt = PromptTemplate(
    input_variables=["task"],
    template="""Generate a solution for: {task}
    
Be thorough and consider edge cases."""
)

# Step 2: Critique the output
critic_prompt = PromptTemplate(
    input_variables=["solution", "task"],
    template="""Review this solution for the task: {task}

Solution: {solution}

Provide specific, actionable feedback on:
1. Correctness
2. Completeness  
3. Edge cases
4. Improvements needed"""
)

# Step 3: Refine based on critique
refiner_prompt = PromptTemplate(
    input_variables=["solution", "critique", "task"],
    template="""Improve this solution based on the critique.

Original Task: {task}
Original Solution: {solution}
Critique: {critique}

Provide an improved solution:"""
)

def reflection_agent(task: str, max_iterations: int = 3):
    """Agentic reflection pattern implementation"""
    
    # Initial generation
    generator = LLMChain(llm=llm, prompt=generator_prompt)
    solution = generator.run(task=task)
    
    for i in range(max_iterations):
        # Critique
        critic = LLMChain(llm=llm, prompt=critic_prompt)
        critique = critic.run(solution=solution, task=task)
        
        # Check if critique indicates quality is sufficient
        if "no major issues" in critique.lower():
            break
            
        # Refine
        refiner = LLMChain(llm=llm, prompt=refiner_prompt)
        solution = refiner.run(
            solution=solution, 
            critique=critique, 
            task=task
        )
    
    return solution