Agentic AI e Operações Autônomas¶

# Exemplo: Framework de agente operacional
from dataclasses import dataclass
from typing import List, Optional, Dict
from enum import Enum

class ActionType(Enum):
    AUTOMATED = "automated"
    APPROVAL_REQUIRED = "approval_required"
    ESCALATE = "escalate"
    NO_ACTION = "no_action"

@dataclass
class Observation:
    source: str
    data: Dict
    timestamp: float
    severity: str

@dataclass
class Decision:
    action_type: ActionType
    action: str
    parameters: Dict
    confidence: float
    reasoning: str
    requires_approval: bool

class OperationsAgent:
    def __init__(self, llm_client, tool_registry, safety_guardrails):
        self.llm = llm_client
        self.tools = tool_registry
        self.guardrails = safety_guardrails
        self.memory = ConversationBufferMemory()

    async def observe(self, event_stream) -> List[Observation]:
        """Coleta e filtra observações relevantes"""
        observations = []
        async for event in event_stream:
            if self._is_relevant(event):
                obs = Observation(
                    source=event.source,
                    data=event.payload,
                    timestamp=event.timestamp,
                    severity=self._classify_severity(event)
                )
                observations.append(obs)
        return observations

    async def orient(self, observations: List[Observation]) -> Dict:
        """Enriquece contexto e correlações"""
        context = {
            "observations": observations,
            "historical_similar": await self._find_similar_incidents(observations),
            "current_topology": await self._get_system_topology(),
            "recent_changes": await self._get_recent_deployments(),
            "business_impact": self._assess_business_impact(observations)
        }
        return context

    async def decide(self, context: Dict) -> Decision:
        """Toma decisão baseada em contexto"""
        # Query LLM com contexto enriquecido
        prompt = self._build_decision_prompt(context)

        llm_response = await self.llm.generate(
            prompt,
            tools=self.tools.describe_all(),
            temperature=0.2  # Baixa temperatura para decisões
        )

        # Aplica guardrails de segurança
        safe_decision = self.guardrails.validate(llm_response)

        return Decision(
            action_type=safe_decision["type"],
            action=safe_decision["action"],
            parameters=safe_decision["params"],
            confidence=safe_decision["confidence"],
            reasoning=safe_decision["explanation"],
            requires_approval=safe_decision["risk_score"] > 0.7
        )

    async def act(self, decision: Decision) -> Dict:
        """Executa ação decidida"""
        if decision.requires_approval:
            return await self._request_human_approval(decision)

        if decision.action_type == ActionType.AUTOMATED:
            result = await self.tools.execute(
                decision.action,
                decision.parameters
            )

            # Registra para aprendizado
            await self._record_outcome(decision, result)

            return result

        elif decision.action_type == ActionType.ESCALATE:
            return await self._escalate_to_human(decision)

    async def learn(self, decision: Decision, outcome: Dict):
        """Atualiza modelo com feedback"""
        # Atualiza memória
        self.memory.add(decision, outcome)

        # Se padrão emergir, propõe atualização de runbook
        if self._detect_pattern(self.memory.recent()):
            await self._suggest_runbook_update()

class ReactiveAgent:
    """Responde a eventos específicos com ações predefinidas"""

    def __init__(self):
        self.rules = {
            "high_cpu": self.scale_up,
            "disk_full": self.cleanup_logs,
            "service_down": self.restart_service
        }

    async def handle_event(self, event):
        if event.type in self.rules:
            action = self.rules[event.type]
            return await action(event.target)

class DeliberativeAgent:
    """Planeja sequências de ações para atingir objetivos"""

    async def achieve_goal(self, goal: str, context: Dict):
        # Decompõe objetivo em sub-tarefas
        plan = await self.llm.generate(
            f"Crie um plano para: {goal}",
            context=context,
            available_tools=self.tools.list()
        )

        for step in plan.steps:
            result = await self.execute_step(step)
            if not result.success:
                plan = await self.replan(plan, step, result)

        return plan.status

class HybridAgent:
    """Combina reatividade para urgências com deliberação para complexidade"""

    async def run(self):
        while True:
            event = await self.event_queue.get()

            if event.severity == "critical":
                # Reação imediata
                await self.reactive_handler(event)
            else:
                # Planejamento cuidadoso
                await self.deliberative_handler(event)

class AgentOrchestrator:
    def __init__(self):
        self.agents = {
            "security": SecurityAgent(),
            "capacity": CapacityAgent(),
            "cost": CostOptimizationAgent(),
            "reliability": ReliabilityAgent()
        }
        self.conflict_resolver = ConflictResolver()

    async def process_situation(self, situation: Dict):
        # Consulta todos os agentes especialistas
        proposals = []
        for name, agent in self.agents.items():
            proposal = await agent.evaluate(situation)
            if proposal.confidence > 0.6:
                proposals.append(proposal)

        # Resolve conflitos
        if len(proposals) > 1:
            final_decision = await self.conflict_resolver.resolve(
                proposals, situation
            )
        else:
            final_decision = proposals[0]

        # Executa com monitoramento
        return await self.execute_with_monitoring(final_decision)

class AutoRemediationAgent:
    """Agente de auto-recuperação para incidentes comuns"""

    async def handle_incident(self, alert: Alert):
        # Análise de causa raiz
        root_cause = await self.analyze_root_cause(alert)

        # Busca runbook aplicável
        runbook = await self.find_runbook(root_cause)

        if runbook and runbook.auto_approved:
            # Executa remediation
            result = await self.execute_runbook(runbook, alert)

            # Notifica e documenta
            await self.notify_team(alert, result, "autoresolved")
            await self.update_incident_record(alert, result)
        else:
            # Escalona para humano com contexto
            await self.escalate_with_context(alert, root_cause)

    async def analyze_root_cause(self, alert: Alert) -> RootCause:
        # Coleta telemetria correlacionada
        logs = await self.query_logs(alert.service, alert.time_window)
        metrics = await self.query_metrics(alert.service, alert.time_window)
        traces = await self.query_traces(alert.service, alert.time_window)

        # Análise por LLM
        analysis = await self.llm.analyze_root_cause(
            alert=alert,
            logs=logs,
            metrics=metrics,
            traces=traces,
            historical_incidents=self.similar_incidents(alert)
        )

        return RootCause(
            category=analysis.category,
            description=analysis.description,
            confidence=analysis.confidence,
            suggested_actions=analysis.actions
        )

class IntelligentCapacityAgent:
    """Prediz necessidades e escala proativamente"""

    async def forecast_and_scale(self):
        # Coleta dados históricos
        usage_patterns = await self.get_usage_history(days=90)
        business_events = await self.get_business_calendar()

        # Predição de demanda
        forecast = await self.llm.forecast(
            historical_data=usage_patterns,
            upcoming_events=business_events,
            external_factors=["marketing_campaigns", "seasonality"]
        )

        # Recomendações de capacity
        recommendations = []
        for service in self.services:
            current_capacity = await self.get_current_capacity(service)
            predicted_peak = forecast.get_peak_for(service)

            if predicted_peak > current_capacity * 0.8:
                recommendations.append({
                    "service": service,
                    "action": "scale_up",
                    "target_capacity": predicted_peak * 1.2,
                    "timing": "48h_before_peak",
                    "justification": forecast.explanation
                })

        # Executa ou propõe
        for rec in recommendations:
            if rec["confidence"] > 0.85:
                await self.schedule_scaling(rec)
            else:
                await self.propose_to_sre_team(rec)

class CostOptimizationAgent:
    """Identifica e executa otimizações de custo"""

    async def optimize_costs(self):
        # Análise de spend
        cost_data = await self.get_detailed_costs()
        utilization = await self.get_resource_utilization()

        # Identifica oportunidades
        opportunities = await self.llm.identify_optimizations(
            costs=cost_data,
            utilization=utilization,
            reserved_instances=self.current_ris,
            savings_plans=self.current_sp
        )

        for opp in opportunities:
            if opp.type == "rightsizing":
                await self.propose_rightsizing(opp)
            elif opp.type == "reserved_capacity":
                await self.propose_ri_purchase(opp)
            elif opp.type == "idle_resource":
                if opp.safety_confidence > 0.95:
                    await self.terminate_resource(opp.resource)
                else:
                    await self.propose_termination(opp)