Agent Designer

#!/usr/bin/env python3
"""
Agent Evaluator - Multi-Agent System Performance Analysis

Takes agent execution logs (task, actions taken, results, time, tokens used) 
and evaluates performance: task success rate, average cost per task, latency 
distribution, error patterns, tool usage efficiency, identifies bottlenecks 
and improvement opportunities.

Input: execution logs JSON
Output: performance report + bottleneck analysis + optimization recommendations
"""

import json
import argparse
import sys
import statistics
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, asdict
from collections import defaultdict, Counter
from datetime import datetime, timedelta
import re


@dataclass
class ExecutionLog:
    """Single execution log entry"""
    task_id: str
    agent_id: str
    task_type: str
    task_description: str
    start_time: str
    end_time: str
    duration_ms: int
    status: str  # success, failure, partial, timeout
    actions: List[Dict[str, Any]]
    results: Dict[str, Any]
    tokens_used: Dict[str, int]  # input_tokens, output_tokens, total_tokens
    cost_usd: float
    error_details: Optional[Dict[str, Any]]
    tools_used: List[str]
    retry_count: int
    metadata: Dict[str, Any]


@dataclass
class PerformanceMetrics:
    """Performance metrics for an agent or system"""
    total_tasks: int
    successful_tasks: int
    failed_tasks: int
    partial_tasks: int
    timeout_tasks: int
    success_rate: float
    failure_rate: float
    average_duration_ms: float
    median_duration_ms: float
    percentile_95_duration_ms: float
    min_duration_ms: int
    max_duration_ms: int
    total_tokens_used: int
    average_tokens_per_task: float
    total_cost_usd: float
    average_cost_per_task: float
    cost_per_token: float
    throughput_tasks_per_hour: float
    error_rate: float
    retry_rate: float


@dataclass
class ErrorAnalysis:
    """Error pattern analysis"""
    error_type: str
    count: int
    percentage: float
    affected_agents: List[str]
    affected_task_types: List[str]
    common_patterns: List[str]
    suggested_fixes: List[str]
    impact_level: str  # high, medium, low


@dataclass
class BottleneckAnalysis:
    """System bottleneck analysis"""
    bottleneck_type: str  # agent, tool, communication, resource
    location: str
    severity: str  # critical, high, medium, low
    description: str
    impact_on_performance: Dict[str, float]
    affected_workflows: List[str]
    optimization_suggestions: List[str]
    estimated_improvement: Dict[str, float]


@dataclass
class OptimizationRecommendation:
    """Performance optimization recommendation"""
    category: str  # performance, cost, reliability, scalability
    priority: str  # high, medium, low
    title: str
    description: str
    implementation_effort: str  # low, medium, high
    expected_impact: Dict[str, Any]
    estimated_cost_savings: Optional[float]
    estimated_performance_gain: Optional[float]
    implementation_steps: List[str]
    risks: List[str]
    prerequisites: List[str]


@dataclass
class EvaluationReport:
    """Complete evaluation report"""
    summary: Dict[str, Any]
    system_metrics: PerformanceMetrics
    agent_metrics: Dict[str, PerformanceMetrics]
    task_type_metrics: Dict[str, PerformanceMetrics]
    tool_usage_analysis: Dict[str, Any]
    error_analysis: List[ErrorAnalysis]
    bottleneck_analysis: List[BottleneckAnalysis]
    optimization_recommendations: List[OptimizationRecommendation]
    trends_analysis: Dict[str, Any]
    cost_breakdown: Dict[str, Any]
    sla_compliance: Dict[str, Any]
    metadata: Dict[str, Any]


class AgentEvaluator:
    """Evaluate multi-agent system performance from execution logs"""
    
    def __init__(self):
        self.error_patterns = self._define_error_patterns()
        self.performance_thresholds = self._define_performance_thresholds()
        self.cost_benchmarks = self._define_cost_benchmarks()
    
    def _define_error_patte