AI-Human Collaboration: The Future of Work with Agents

# Traditional tool usage
class AITool:
    """AI enhances human capability, human remains in control"""
    
    def assist(self, task: Task) -> Result:
        # Human decides what to ask
        prompt = human.create_prompt(task)
        
        # AI provides suggestions
        suggestion = self.model.generate(prompt)
        
        # Human reviews and decides
        final = human.evaluate_and_select(suggestion)
        
        return final

# Use cases
TOOL_USE_CASES = [
    "Research and information retrieval",
    "Writing assistance and editing",
    "Data analysis and visualization",
    "Code completion and debugging",
    "Translation and localization"
]

# AI assistant model
class AIAssistant:
    """AI handles tasks, human approves and oversees"""
    
    async def handle(self, request: Request) -> Response:
        # AI processes request
        result = await self.execute(request)
        
        # Check if approval needed
        if self.needs_approval(result):
            # Get human approval
            approved = await human.approve(result)
            
            if not approved:
                return await self.refine(result, human.feedback)
        
        return result

# Assistant use cases
ASSISTANT_USE_CASES = [
    "Email management and drafting",
    "Meeting scheduling",
    "Report generation",
    "Customer inquiry handling",
    "Data processing and transformation"
]

# Equal partner model
class AITeammate:
    """AI and human collaborate as equals on complex tasks"""
    
    async def collaborate(self, project: Project, human: Human) -> Output:
        # Define roles based on strengths
        roles = self.assign_roles(project)
        
        # Work in parallel or sequence
        if roles.can_parallelize:
            # Parallel work
            ai_work = await self.execute(roles.ai_tasks)
            human_work = await human.execute(roles.human_tasks)
            
            # Integrate
            result = await self.integrate(ai_work, human_work)
        else:
            # Sequential collaboration
            human_work = await human.execute(project.phase1)
            ai_work = await self.execute(project.phase2, context=human_work)
            result = await self.finalize(ai_work, human_work)
        
        # Mutual review
        await human.review(result)
        await self.review(result)
        
        return result

# Teammate use cases
TEAMMATE_USE_CASES = [
    "Research and development",
    "Strategic planning",
    "Creative projects",
    "Complex problem solving",
    "Customer relationship management"
]

# Intelligent role assignment
class RoleAssigner:
    def assign_roles(self, task: Task, team: list) -> RoleAssignment:
        # Analyze task requirements
        requirements = self.analyze_task(task)
        
        # Assess team capabilities
        capabilities = self.assess_capabilities(team)
        
        # Match requirements to capabilities
        assignments = []
        
        for req in requirements:
            best_match = self.find_best_match(req, capabilities)
            assignments.append(Role(
                requirement=req,
                assignee=best_match,
                collaboration=req.collaboration_type
            ))
        
        return RoleAssignment(
            task=task,
            assignments=assignments,
            workflow=self.determine_workflow(assignments)
        )
    
    def analyze_task(self, task: Task) -> list:
        return [
            Requirement(
                type="creative",
                human_weight=0.8,
                ai_weight=0.2
            ),
            Requirement(
                type="analysis", 
                human_weight=0.3,
                ai_weight=0.7
            ),
            Requirement(
                type="decision",
                human_weight=0.6,
                ai_weight=0.4
            )
        ]
    
    def assess_capabilities(self, team: list) -> dict:
        return {
            "human": {
                "creativity": 0.9,
                "judgment": 0.95,
                "empathy": 0.9,
                "speed": 0.4
            },
            "ai": {
                "creativity": 0.7,
                "judgment": 0.8,
                "empathy": 0.3,
                "speed": 0.99
            }
        }

# Effective prompt patterns
class HumanAIPrompt:
    @staticmethod
    def task_prompt(task: str, context: str = None, constraints: list = None) -> str:
        prompt = f"""
Task: {task}
"""
        if context:
            prompt += f"\nContext: {context}"
        
        if constraints:
            prompt += f"\nConstraints: {', '.join(constraints)}"
        
        prompt += "\n\nProvide your response."
        
        return prompt
    
    @staticmethod
    def collaborative_prompt(task: str, human_part: str) -> str:
        return f"""
Task: {task}

I'll handle: {human_part}

Please handle the rest and collaborate with me on integrating our work.
"""
    
    @staticmethod
    def feedback_prompt(previous_output: str, feedback: str) -> str:
        return f"""
Previous output:
{previous_output}

Feedback:
{feedback}

Please revise based on the feedback.
"""

# Best practices
PROMPT_BEST_PRACTICES = [
    "Be specific about desired output",
    "Provide relevant context",
    "State constraints explicitly",
    "Indicate collaboration style",
    "Give feedback for improvement",
    "Acknowledge AI contributions"
]

# AI communication patterns
class AIToHumanCommunication:
    def __init__(self):
        self.confidence_threshold = 0.8
    
    async def present_recommendation(self, analysis: Analysis) -> Presentation:
        # Format for human understanding
        presentation = {
            "summary": await self.summarize(analysis),
            "details": self.format_details(analysis),
            "recommendation": analysis.recommendation,
            "confidence": analysis.confidence,
            "alternatives": analysis.alternatives,
            "questions": await self.identify_questions(analysis)
        }
        
        # Highlight confidence
        if analysis.confidence < self.confidence_threshold:
            presentation["note"] = "This recommendation has uncertainty - please review carefully"
        
        return Presentation(**presentation)
    
    async def request_input(self, question: Question) -> Request:
        return Request(
            question=question.text,
            context=question.context,
            options=question.options,
            urgency=question.urgency,
            deadline=question.deadline
        )
    
    async def escalate(self, issue: Issue) -> Escalation:
        return Escalation(
            summary=issue.summary,
            details=issue.details,
            urgency=issue.urgency,
            suggested_escalate_to=issue.suggested_contact,
            relevant_data=issue.supporting_data
        )

# Agent management
class AgentSupervisor:
    def __init__(self, agents: list):
        self.agents = agents
        self.task_queue = TaskQueue()
        self.performance = PerformanceTracker()
    
    async def assign_task(self, task: Task, agent: Agent) -> Assignment:
        # Check agent availability
        if not await agent.is_available():
            # Find alternative
            agent = await self.find_available_agent()
        
        # Assign with context
        assignment = await agent.assign(task)
        
        # Track
        await self.performance.log_assignment(assignment)
        
        return assignment
    
    async def monitor(self, agent: Agent, task: Task) -> MonitoringResult:
        # Check progress
        status = await agent.check_status(task)
        
        # Log metrics
        metrics = await self.performance.collect(agent, task)
        
        # Check for issues
        if status.progress < 0.1 and status.elapsed > expected_time * 2:
            return MonitoringResult(
                status="stuck",
                recommendation="Consider reassigning or providing guidance"
            )
        
        return MonitoringResult(status="on_track", metrics=metrics)
    
    async def review_output(self, agent: Agent, output: Output) -> Review:
        # Evaluate output
        evaluation = await self.evaluate(output)
        
        # Provide feedback
        if evaluation.requires_human_review:
            reviewed = await self.human.review(output)
            feedback = await self.provide_feedback(agent, reviewed)
        
        # Log for learning
        await self.performance.log_result(agent, output, evaluation)
        
        return evaluation

# AI agent performance metrics
class AgentPerformanceMetrics:
    def __init__(self):
        self.metrics = {}
    
    async def track(self, agent_id: str, task: Task, result: Result):
        if agent_id not in self.metrics:
            self.metrics[agent_id] = []
        
        self.metrics[agent_id].append({
            "task_type": task.type,
            "success": result.success,
            "quality": result.quality_score,
            "time": result.duration,
            "human_feedback": result.feedback
        })
    
    def get_performance_report(self, agent_id: str) -> Report:
        tasks = self.metrics.get(agent_id, [])
        
        return Report(
            agent_id=agent_id,
            total_tasks=len(tasks),
            success_rate=sum(1 for t in tasks if t["success"]) / len(tasks) if tasks else 0,
            avg_quality=sum(t["quality"] for t in tasks) / len(tasks) if tasks else 0,
            avg_time=sum(t["time"] for t in tasks) / len(tasks) if tasks else 0,
            human_satisfaction=sum(t["human_feedback"] for t in tasks) / len(tasks) if tasks else 0
        )

# Building hybrid team culture
HYBRID_TEAM_PRACTICES = {
    "leadership": [
        "Model AI collaboration",
        "Set clear expectations",
        "Celebrate AI successes and failures",
        "Invest in training"
    ],
    
    "processes": [
        "Define human-AI workflows",
        "Establish approval gates",
        "Create feedback loops",
        "Monitor performance"
    ],
    
    "culture": [
        "Treat AI as teammate, not tool",
        "Encourage experimentation",
        "Normalize AI mistakes",
        "Value human judgment"
    ],
    
    "training": [
        "Prompt engineering skills",
        "AI evaluation skills",
        "Collaboration practices",
        "Critical thinking"
    ]
}

# Dynamic trust management
class TrustManager:
    def __init__(self):
        self.trust_scores = {}
    
    def calculate_trust(self, agent: Agent, task: Task) -> float:
        # Base trust on track record
        track_record = self.get_track_record(agent)
        
        # Adjust for task complexity
        task_factor = self.get_task_complexity_factor(task)
        
        # Consider uncertainty
        uncertainty = self.get_uncertainty(agent, task)
        
        # Calculate
        trust = track_record * task_factor * (1 - uncertainty)
        
        return max(0, min(1, trust))
    
    def adjust_trust(self, agent: Agent, interaction: Interaction):
        if interaction.successful:
            # Increase trust slightly
            self.trust_scores[agent.id] = min(
                1.0,
                self.trust_scores.get(agent.id, 0.5) + 0.05
            )
        else:
            # Decrease trust
            self.trust_scores[agent.id] = max(
                0,
                self.trust_scores.get(agent.id, 0.5) - 0.1
            )

# Future collaboration patterns
FUTURE_PATTERNS = {
    "2026": [
        "AI becomes default workspace assistant",
        "Human-AI pairing becomes common in knowledge work",
        "Agent coordination becomes a skill"
    ],
    
    "2028": [
        "AI team members have persistent identity",
        "Emotional AI for better collaboration",
        "Neural interfaces for seamless collaboration"
    ],
    
    "2030": [
        "Human-AI merger for some tasks",
        "Telepathic-level communication with AI",
        "AI as full team members with rights"
    ]
}