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Problem Solving & Decision Making with Google ADK

Autonomous agents that reason through complex problems, evaluate alternatives, and make decisions using multi-step reasoning.

Overview

ADK enables agents to solve complex problems by breaking them into sub-problems, reasoning about solutions, and choosing optimal actions from multiple alternatives.

Problem-Solving Agent Framework

Python - Problem Solver
from google_adk import Agent, Reasoning, OptionEvaluation
from typing import List, Dict, Any

class ProblemSolverAgent(Agent):
    def __init__(self):
        super().__init__()
        self.reasoning_engine = Reasoning()
        self.evaluator = OptionEvaluation()
    
    @Tool(description="Generate possible solutions")
    def brainstorm_solutions(self, problem: str, constraints: List[str]) -> List[str]:
        """Generate creative solutions"""
        return ["Solution 1: ...", "Solution 2: ...", "Solution 3: ..."]
    
    @Tool(description="Evaluate solution feasibility")
    def evaluate_solution(self, solution: str, criteria: Dict) -> Dict:
        """Score solution against criteria"""
        return {
            "feasibility": 0.85,
            "cost": 1000,
            "time_estimate": "5 days",
            "risk_level": "medium"
        }
    
    async def solve_problem(self, problem: str, constraints: List[str] = None):
        """Agent autonomously solves problem through reasoning"""
        
        # Step 1: Understand and decompose problem
        analysis = await self.reasoning_engine.analyze(problem)
        
        # Step 2: Generate candidate solutions
        solutions = self.brainstorm_solutions(problem, constraints or [])
        
        # Step 3: Evaluate each solution
        evaluations = []
        for solution in solutions:
            eval_result = self.evaluate_solution(solution, {
                "cost": {"max": 5000},
                "time": {"max": "10 days"},
                "risk": {"acceptable": ["low", "medium"]}
            })
            evaluations.append({
                "solution": solution,
                "evaluation": eval_result,
                "score": self.calculate_score(eval_result)
            })
        
        # Step 4: Select best solution
        best = max(evaluations, key=lambda x: x["score"])
        
        return {
            "problem": problem,
            "analysis": analysis,
            "solutions_considered": len(solutions),
            "recommended": best["solution"],
            "score": best["score"],
            "all_evaluations": evaluations
        }
    
    def calculate_score(self, eval_result: Dict) -> float:
        """Score solution based on criteria"""
        return eval_result.get("feasibility", 0.5)

Decision-Making with Trade-offs

Python - Decision Making Agent
from google_adk import Agent, DecisionTree, TradeOffAnalysis

class DecisionMakingAgent(Agent):
    def __init__(self):
        super().__init__()
        self.decision_tree = DecisionTree()
        self.tradeoff_analyzer = TradeOffAnalysis()
    
    async def make_strategic_decision(self, context: Dict, options: List[Dict]):
        """Agent makes decision considering trade-offs"""
        
        # Analyze trade-offs between options
        analysis = await self.tradeoff_analyzer.analyze(
            options=options,
            objectives=["maximize_profit", "minimize_risk", "maximize_customer_satisfaction"]
        )
        
        # Reasoning about best choice
        decision = await self.decision_tree.evaluate(
            options=options,
            analysis=analysis,
            context=context
        )
        
        return {
            "options_evaluated": len(options),
            "tradeoffs": analysis,
            "recommended_option": decision["best_option"],
            "reasoning": decision["rationale"]
        }

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