Autonomous Research Agents

class ResearchOrchestrator:
    """Coordinates autonomous research workflow"""
    
    def __init__(self, tools: ToolRegistry):
        self.tools = tools
        self.state = ResearchState()
        self.max_iterations = 5
        
    async def research(self, topic: str, 
                      depth: str = 'comprehensive') -> Report:
        """Execute autonomous research on topic"""
        
        # Initialize research state
        self.state.topic = topic
        self.state.questions = self._generate_questions(topic)
        
        # Research loop
        for iteration in range(self.max_iterations):
            print(f"Research Iteration {iteration + 1}/{self.max_iterations}")
            
            # Phase 1: Search for answers
            sources = await self._search_phase()
            
            # Phase 2: Extract information
            findings = await self._extract_phase(sources)
            
            # Phase 3: Synthesize insights
            insights = await self._synthesize_phase(findings)
            
            # Phase 4: Decide whether to continue
            if self._should_conclude(insights):
                break
            
            # Generate follow-up questions for next iteration
            self.state.questions = self._generate_followup_questions(
                insights
            )
        
        # Generate final report
        report = await self._generate_report()
        return report
    
    def _should_conclude(self, insights: List[Insight]) -> bool:
        """Decide if research is complete"""
        # Check confidence level
        avg_confidence = np.mean([i.confidence for i in insights])
        if avg_confidence < 0.8:
            return False
        
        # Check coverage (all questions answered?)
        answered = sum(1 for q in self.state.questions if q.answered)
        coverage = answered / len(self.state.questions)
        
        return coverage >= 0.9  # 90% questions answered