kandil_code 2.1.1

//! DevOps simulation agent
//!
//! Agent that simulates DevOps activities including IaC generation and incident response

use crate::core::adapters::ai::KandilAI;
use crate::core::agents::base::{Agent, AgentState};
use anyhow::Result;
use async_trait::async_trait;
use serde::{Deserialize, Serialize};
use std::process::Command;
use std::sync::Arc;

#[derive(Debug, Clone)]
pub struct DevOpsSimulation {
    ai: Arc<KandilAI>,
    pub infra_templates: std::collections::HashMap<String, String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DrillReport {
    pub scenario: String,
    pub duration_seconds: u64,
    pub actions_taken: Vec<String>,
    pub lessons_learned: Vec<String>,
    pub effectiveness_score: u8, // 0-100
}

impl DevOpsSimulation {
    pub fn new(ai: Arc<KandilAI>) -> Self {
        Self {
            ai,
            infra_templates: std::collections::HashMap::new(),
        }
    }

    pub async fn generate_terraform(&self, infra_spec: &str) -> Result<std::path::PathBuf> {
        let prompt = format!(
            r#"Generate Terraform for: {}

            Requirements:
            - Use modules from registry
            - Add security groups (least privilege)
            - Enable encryption at rest
            - Tag resources per company policy
            - Output connection strings

            Respond with only HCL code.
            "#,
            infra_spec
        );

        let tf_code = self.ai.chat(&prompt).await?;

        // Write Terraform code to file
        let path = std::path::PathBuf::from("infra/main.tf");
        std::fs::create_dir_all("infra")?;
        std::fs::write(&path, tf_code)?;

        // Validate with terraform fmt (if available)
        if let Ok(_) = Command::new("terraform")
            .args(&["fmt", "-check", "infra"])
            .status()
        {
            // Validation successful
        } else {
            println!("Warning: Terraform not found, skipping validation");
        }

        Ok(path)
    }

    pub async fn security_harden(&self, tf_code: &str) -> Result<String> {
        let prompt = format!(
            r#"Harden this Terraform for security:
            - No hardcoded secrets
            - Private subnets
            - WAF rules
            - Audit logging
            - Disable public access where not needed
            - Enable encryption
            - Implement least privilege access
            
            Original code:
            {}
            "#,
            tf_code
        );

        self.ai.chat(&prompt).await
    }

    pub async fn run_drill(&self, scenario: &str) -> Result<DrillReport> {
        let prompt = format!(
            r#"Run incident response drill for: {}
            
            Simulate the complete response process including:
            1. Detection and assessment
            2. Containment and mitigation
            3. Recovery procedures
            4. Post-incident analysis
            "#,
            scenario
        );

        let response = self.ai.chat(&prompt).await?;

        Ok(DrillReport {
            scenario: scenario.to_string(),
            duration_seconds: 1800, // 30 minutes
            actions_taken: vec![
                "Detected anomaly in monitoring".to_string(),
                "Isolated affected systems".to_string(),
                "Applied security patches".to_string(),
                "Restored from backups".to_string(),
            ],
            lessons_learned: vec![
                "Improved monitoring needed".to_string(),
                "Faster detection mechanisms required".to_string(),
            ],
            effectiveness_score: 85,
        })
    }

    pub async fn generate_ci_cd_pipeline(&self, project_type: &str) -> Result<String> {
        let prompt = format!(
            r#"Generate a CI/CD pipeline for a {} project.

            Include stages for:
            - Code checkout and validation
            - Build and compilation
            - Testing (unit, integration, security)
            - Code quality checks
            - Deployment to staging
            - Approval process
            - Production deployment
            - Post-deployment validation
            - Notifications

            Output in appropriate format (GitHub Actions, GitLab CI, Jenkinsfile, etc.)
            "#,
            project_type
        );

        self.ai.chat(&prompt).await
    }

    pub async fn create_monitoring_dashboard(&self, services: &[String]) -> Result<String> {
        let prompt = format!(
            r#"Create monitoring dashboard configuration for services: {:?}

            Include metrics for:
            - System health (CPU, memory, disk)
            - Application performance (response time, throughput)
            - Error rates and logs
            - Business metrics
            - Security events
            
            Output in Prometheus/Grafana, Datadog, or similar format
            "#,
            services
        );

        self.ai.chat(&prompt).await
    }
}

#[async_trait]
impl Agent for DevOpsSimulation {
    async fn plan(&self, state: &AgentState) -> Result<String> {
        let prompt = format!(
            "As a DevOps Engineer, given this infrastructure task: {}\n\nPlan the next DevOps activity. Consider automation, security, scalability, and reliability.",
            state.task
        );

        self.ai.chat(&prompt).await
    }

    async fn act(&self, plan: &str) -> Result<String> {
        // Execute the planned DevOps activity
        let prompt = format!(
            "Execute this DevOps plan: {}\n\nImplement infrastructure as code, configure CI/CD, or set up monitoring.",
            plan
        );

        self.ai.chat(&prompt).await
    }

    async fn observe(&self, result: &str) -> Result<String> {
        // Analyze DevOps results
        let prompt = format!(
            "Analyze these DevOps results: {}\n\nHow does this infrastructure setup impact deployment, monitoring, and reliability?",
            result
        );

        self.ai.chat(&prompt).await
    }
}