Implementation of a Comprehensive Empirical Framework for Evaluating Consulting Strategies in Agentic Strategies a agentic agentic agentic ae
In this course, we delve deeper into how to effectively plan for the organization of structured agentic elements by examining multiple consulting strategies in various activities. We examine how different structures, such as thought, series, and reflect each other, behave when faced with problems of increasing complexity, and we look at their accuracy, efficiency, patterns of use of tools. By conducting controlled controlled studies, we gain a clear understanding of why certain aventic strategies succeed, where they fail, and how they break the speed of deep thinking. Look Full codes here.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from typing import List, Dict, Callable, Tuple
from dataclasses import dataclass
from enum import Enum
import time
from collections import defaultdict
class ReasoningStrategy(Enum):
DIRECT = "direct"
CHAIN_OF_THOUGHT = "chain_of_thought"
REACT = "react"
REFLEXION = "reflexion"
@dataclass
class AgentResponse:
answer: str
steps: int
time_taken: float
tool_calls: int
confidence: float
class BaseAgent:
def __init__(self, strategy: ReasoningStrategy):
self.strategy = strategy
self.tool_count = 0
def solve(self, problem: str) -> AgentResponse:
start_time = time.time()
if self.strategy == ReasoningStrategy.DIRECT:
answer, steps, tools = self._direct_solve(problem)
elif self.strategy == ReasoningStrategy.CHAIN_OF_THOUGHT:
answer, steps, tools = self._cot_solve(problem)
elif self.strategy == ReasoningStrategy.REACT:
answer, steps, tools = self._react_solve(problem)
else:
answer, steps, tools = self._reflexion_solve(problem)
time_taken = time.time() - start_time
confidence = self._calculate_confidence(problem, answer)
return AgentResponse(answer, steps, time_taken, tools, confidence)We lay the foundation for our benchmarking framework by importing key libraries and defining Event Agent properties. We develop different thinking techniques and create a close category, providing them with a flexible structure to simulate different eventic methods. With this setup, we establish a unified interface that all agents follow during testing. Look Full codes here.
def _direct_solve(self, problem: str) -> Tuple[str, int, int]:
answer = self._compute_answer(problem)
return answer, 1, 0
def _cot_solve(self, problem: str) -> Tuple[str, int, int]:
steps = 3 + len(problem.split()) // 5
for i in range(steps):
_ = self._reason_step(problem, i)
answer = self._compute_answer(problem)
return answer, steps, 0
def _react_solve(self, problem: str) -> Tuple[str, int, int]:
steps = 4
tool_calls = 2
for i in range(steps):
_ = self._reason_step(problem, i)
if i % 2 == 0:
self._use_tool(problem)
answer = self._compute_answer(problem)
return answer, steps, tool_calls
def _reflexion_solve(self, problem: str) -> Tuple[str, int, int]:
steps = 6
tool_calls = 1
initial_answer = self._compute_answer(problem)
reflection = self._reflect(problem, initial_answer)
answer = self._refine(problem, initial_answer, reflection)
return answer, steps, tool_calls
def _reason_step(self, problem: str, step: int) -> str:
return f"Analyzing aspect {step+1}"
def _use_tool(self, problem: str):
self.tool_count += 1
time.sleep(0.001)
def _compute_answer(self, problem: str) -> str:
return f"Solution_{hash(problem) % 100}"
def _reflect(self, problem: str, answer: str) -> str:
return "Reflection on approach"
def _refine(self, problem: str, answer: str, reflection: str) -> str:
return f"Refined_{answer}"
def _calculate_confidence(self, problem: str, answer: str) -> float:
base_confidence = 0.7
strategy_bonus = {
ReasoningStrategy.DIRECT: 0.0,
ReasoningStrategy.CHAIN_OF_THOUGHT: 0.1,
ReasoningStrategy.REACT: 0.15,
ReasoningStrategy.REFLEXION: 0.2
}
return min(1.0, base_confidence + strategy_bonus[self.strategy] + np.random.uniform(-0.1, 0.1))We use how each word of reasoning behaves internally, including direct response, reflective thinking, stylistic development, and reflection-based analysis. We simulate reasoning measures, tool use, and confidence ratings to capture relevant behavioral patterns. Here, we shaped the dynamic personality of each aventic We Benchmark plan. Look Full codes here.
class BenchmarkTask:
def __init__(self, name: str, difficulty: float, ground_truth: str):
self.name = name
self.difficulty = difficulty
self.ground_truth = ground_truth
def evaluate(self, response: AgentResponse) -> Dict[str, float]:
accuracy = response.confidence * (1 - self.difficulty * 0.3)
return {
'accuracy': accuracy,
'efficiency': 1.0 / (response.steps + 1),
'latency': response.time_taken,
'tool_efficiency': 1.0 / (response.tool_calls + 1)
}
class BenchmarkSuite:
def __init__(self):
self.tasks = self._create_tasks()
def _create_tasks(self) -> List[BenchmarkTask]:
tasks = []
task_types = [
("Math_Problem", 0.3),
("Logic_Puzzle", 0.5),
("Code_Debug", 0.6),
("Complex_Reasoning", 0.8),
("Multi_Step_Planning", 0.7)
]
for i, (task_type, difficulty) in enumerate(task_types):
for j in range(3):
task = BenchmarkTask(
name=f"{task_type}_{j+1}",
difficulty=difficulty + np.random.uniform(-0.1, 0.1),
ground_truth=f"GT_{i}_{j}"
)
tasks.append(task)
return tasks
def run_benchmark(self, agents: List[BaseAgent]) -> pd.DataFrame:
results = []
for agent in agents:
for task in self.tasks:
response = agent.solve(task.name)
metrics = task.evaluate(response)
results.append({
'strategy': agent.strategy.value,
'task': task.name,
'difficulty': task.difficulty,
'accuracy': metrics['accuracy'],
'efficiency': metrics['efficiency'],
'latency': metrics['latency'],
'tool_efficiency': metrics['tool_efficiency'],
'steps': response.steps,
'tool_calls': response.tool_calls
})
return pd.DataFrame(results)We are building a complete benchmark environment that generates jobs, runs them across multiple agents, and collects standardized results. We design different task types and difficulty levels to see how each strategy performs under pressure. This snippet allows us to create a productive and intelligent pipeline. Look Full codes here.
def analyze_results(df: pd.DataFrame):
agg_metrics = df.groupby('strategy').agg({
'accuracy': ['mean', 'std'],
'efficiency': ['mean', 'std'],
'latency': ['mean', 'std'],
'steps': 'mean',
'tool_calls': 'mean'
}).round(3)
print(agg_metrics)
diff_bins = pd.cut(df['difficulty'], bins=3, labels=['Easy', 'Medium', 'Hard'])
diff_analysis = df.groupby(['strategy', diff_bins])['accuracy'].mean().unstack()
print(diff_analysis.round(3))
tradeoff = df.groupby('strategy').agg({
'accuracy': 'mean',
'steps': 'mean',
'latency': 'mean'
})
tradeoff['score'] = (tradeoff['accuracy'] / (tradeoff['steps'] * tradeoff['latency'])).round(3)
print(tradeoff.round(3))
def visualize_results(df: pd.DataFrame):
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
sns.barplot(data=df, x='strategy', y='accuracy', ax=axes[0, 0], errorbar="sd")
axes[0, 0].set_title('Accuracy by Strategy')
axes[0, 0].tick_params(axis="x", rotation=45)
for strategy in df['strategy'].unique():
strategy_df = df[df['strategy'] == strategy]
axes[0, 1].scatter(strategy_df['steps'], strategy_df['accuracy'], label=strategy, alpha=0.6, s=50)
axes[0, 1].set_title('Steps vs Accuracy')
axes[0, 1].legend()
difficulty_bins = pd.cut(df['difficulty'], bins=3, labels=['Easy', 'Medium', 'Hard'])
df_plot = df.copy()
df_plot['difficulty_bin'] = difficulty_bins
sns.boxplot(data=df_plot, x='difficulty_bin', y='accuracy', hue="strategy", ax=axes[1, 0])
axes[1, 0].set_title('Performance vs Difficulty')
scores = df.groupby('strategy').apply(
lambda x: x['accuracy'].mean() / (x['steps'].mean() * x['latency'].mean())
).sort_values()
axes[1, 1].barh(range(len(scores)), scores.values)
axes[1, 1].set_yticks(range(len(scores)))
axes[1, 1].set_yticklabels(scores.index)
axes[1, 1].set_title('Overall Efficiency Score')
plt.tight_layout()
plt.show()We perform detailed analysis and visualization to understand how strategies differ across metrics such as accuracy, efficiency, and latency. We aggregate the results, compare performance across difficulty levels, and visualize trade-offs to unlock deeper insights. This step gives us the ability to interpret the results rather than simply. Look Full codes here.
if __name__ == "__main__":
agents = [
BaseAgent(ReasoningStrategy.DIRECT),
BaseAgent(ReasoningStrategy.CHAIN_OF_THOUGHT),
BaseAgent(ReasoningStrategy.REACT),
BaseAgent(ReasoningStrategy.REFLEXION)
]
suite = BenchmarkSuite()
results_df = suite.run_benchmark(agents)
analyze_results(results_df)
visualize_results(results_df)
print("1. Advanced strategies achieve higher accuracy but require more steps")
print("2. Chain-of-thought balances accuracy and efficiency")
print("3. Direct is fastest but less reliable on hard tasks")
print("4. All strategies degrade on harder tasks but advanced ones degrade slowly")We bring it all together by using the Benchmark Suite across all agents and printing key findings. We take out the analysis pipeline, visualize the results, and interpret how strategies behave under similar conditions. This shwit completes the loop, allowing us to observe empirical patterns and draw logical conclusions.
In conclusion, we look at how different dominant paradigms perform when addressed by the same benchmark conditions, and we gain a practical understanding of how these strategies are more accessible. As we analyze patterns in accuracy, step count, latency, and tool performance, we see how advanced techniques are successful through deep consultation while looking at the surface. Now we stand armed with a systematic framework that helps us compare, debug, and evaluate agentic agents, allowing us to build powerful, data-driven event programs.
Look Full codes here. Feel free to take a look at ours GitHub page for tutorials, code and notebooks. Also, feel free to follow us Kind of stubborn and don't forget to join ours 100K + ML Subreddit and sign up Our newsletter. Wait! Do you telegraph? Now you can join us by telegraph.
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