AI-Augmented Developer Platform

interface ScaffoldRequest {
  naturalLanguage: string;
  userId: string;
  team: string;
}

interface ScaffoldPlan {
  template: string;
  parameters: Record<string, any>;
  capabilities: string[];
  estimatedTime: string;
  warnings: string[];
}

class NLScaffoldService {
  private llm: LLMClient;
  private templateRegistry: TemplateRegistry;
  private policyEngine: PolicyEngine;

  async generatePlan(request: ScaffoldRequest): Promise<ScaffoldPlan> {
    // 1. Build context for LLM
    const context = await this.buildContext(request);

    // 2. Prompt LLM to extract intent
    const prompt = `
You are a platform engineering assistant. Convert the user's natural language request into a scaffold plan.

Available templates:
${this.templateRegistry.list().map(t => `- ${t.name}: ${t.description}`).join('
')}

Available capabilities: postgresql, redis, kafka, service-mesh, pubsub, blob-storage, ml-feature-store

User request: "${request.naturalLanguage}"
User team: ${request.team}

Output JSON with:
- template: template name
- parameters: key-value pairs for template
- capabilities: list of required capabilities
- estimatedTime: human-readable estimate
- warnings: any concerns
`;

    const response = await this.llm.complete({
      prompt,
      temperature: 0.1,
      maxTokens: 1000,
      responseFormat: "json",
    });

    const plan = JSON.parse(response.text) as ScaffoldPlan;

    // 2. Validate against policies
    const validation = await this.policyEngine.validatePlan(plan, request.team);
    if (!validation.valid) {
      plan.warnings.push(...validation.violations.map(v => `Policy: ${v.message}`));
    }

    // 3. Enrich with defaults
    plan.parameters = this.enrichWithDefaults(plan.parameters, request.team);

    return plan;
  }

  async executePlan(plan: ScaffoldPlan, userId: string): Promise<ScaffoldResult> {
    // Human-in-the-loop: present plan for approval
    const approval = await this.requestApproval(plan);
    if (!approval.approved) {
      throw new Error("Scaffold cancelled by user");
    }

    // Execute via platform API
    return this.platformClient.scaffold({
      template: plan.template,
      parameters: plan.parameters,
      requestedBy: userId,
    });
  }
}

$ platform scaffold "Create a payment processing service with PostgreSQL and Kafka for the payments team"
🤖 Analyzing request...

📋 Generated Plan:
  Template: golden-path-go-service
  Parameters:
    service_name: payment-processor
    team: payments
    language: go
    framework: gin
  Capabilities: [postgresql, kafka]
  Estimated Time: 3 minutes
  Warnings: None

❓ Proceed? [Y/n] y

🚀 Scaffolding...
  ✓ Validated parameters
  ✓ Rendered template
  ✓ Created GitHub repo: github.com/myorg/payment-processor
  ✓ Configured CI/CD (GitHub Actions)
  ✓ Created ArgoCD Application
  ✓ Provisioned PostgreSQL (db-payments-prod)
  ✓ Provisioned Kafka topic (payments.events)
  ✓ Registered in software catalog
  ✓ Created preview environment

✅ Service created successfully!
  Repository: https://github.com/myorg/payment-processor
  ArgoCD: https://argocd.platform.example.com/applications/payments-payment-processor
  Grafana: https://grafana.platform.example.com/d/service-payment-processor
  Runbook: https://github.com/myorg/payment-processor/blob/main/RUNBOOK.md

Next steps:
  1. git clone https://github.com/myorg/payment-processor
  2. Open in VS Code (DevContainer recommended)
  3. Start coding! Push to main to deploy.

interface PolicyViolation {
  policy: string;
  rule: string;
  message: string;
  file: string;
  line: number;
  severity: "error" | "warning";
  context: string; // Surrounding code
}

interface FixProposal {
  violation: PolicyViolation;
  fix: string; // Unified diff
  explanation: string;
  confidence: number; // 0-1
  requiresHumanReview: boolean;
}

class AutoFixService {
  private llm: LLMClient;
  private gitClient: GitClient;

  async analyzeViolation(violation: PolicyViolation): Promise<FixProposal> {
    const prompt = `
You are a platform policy expert. Fix this policy violation.

Policy: ${violation.policy}
Rule: ${violation.rule}
Message: ${violation.message}
File: ${violation.file}
Line: ${violation.line}
Context:
```
${violation.context}
```

Common patterns for this policy:
${this.getPolicyPatterns(violation.policy, violation.rule)}

Output JSON:
{
  "fix": "unified diff",
  "explanation": "what and why",
  "confidence": 0.0-1.0,
  "requiresHumanReview": true/false
}
`;

    const response = await this.llm.complete({
      prompt,
      temperature: 0.1,
      responseFormat: "json",
    });

    return JSON.parse(response.text);
  }

  async createFixPR(violation: PolicyViolation, proposal: FixProposal): Promise<string> {
    const branchName = `policy-fix/${violation.policy}/${violation.rule}/${Date.now()}`;
    
    // Create branch, apply fix, commit
    await this.gitClient.createBranch(branchName);
    await this.gitClient.applyDiff(violation.file, proposal.fix);
    await this.gitClient.commit(
      `fix: resolve ${violation.policy} ${violation.rule} violation

${proposal.explanation}

Auto-generated by platform AI. Requires review.`,
      branchName
    );

    // Create PR
    const pr = await this.gitClient.createPullRequest({
      title: `fix: ${violation.policy} ${violation.rule} violation in ${violation.file}`,
      body: `## Auto-Fix for Policy Violation

**Policy:** ${violation.policy}
**Rule:** ${violation.rule}
**File:** ${violation.file}:${violation.line}

**Explanation:** ${proposal.explanation}

**Confidence:** ${(proposal.confidence * 100).toFixed(0)}%

**Requires Human Review:** ${proposal.requiresHumanReview ? 'Yes' : 'No'}

```diff
${proposal.fix}
```

*Generated by Platform AI. Please review carefully before merging.*
      `,
      head: branchName,
      base: "main",
      labels: ["auto-fix", "policy-violation", `policy:${violation.policy}`],
      reviewers: ["platform-team"],
    });

    return pr.url;
  }
}

import pandas as pd
import numpy as np
from prophet import Prophet
from sklearn.ensemble import IsolationForest
import mlflow

class CapacityForecaster:
    def __init__(self):
        self.models = {}
        self.anomaly_detector = IsolationForest(contamination=0.01)
    
    def train_capacity_model(self, metric_name: str, df: pd.DataFrame):
        """Train Prophet model for capacity metric (CPU, memory, storage)."""
        # Prepare data for Prophet
        df_prophet = df.rename(columns={'timestamp': 'ds', 'value': 'y'})
        
        # Add regressors: day of week, hour, deployment events
        df_prophet['day_of_week'] = df_prophet['ds'].dt.dayofweek
        df_prophet['hour'] = df_prophet['ds'].dt.hour
        
        model = Prophet(
            yearly_seasonality=False,
            weekly_seasonality=True,
            daily_seasonality=True,
            changepoint_prior_scale=0.05,
            seasonality_prior_scale=10,
        )
        model.add_regressor('day_of_week')
        model.add_regressor('hour')
        
        model.fit(df_prophet)
        self.models[metric_name] = model
        
        # Log to MLflow
        mlflow.log_model(model, f"capacity-{metric_name}")
    
    def forecast(self, metric_name: str, horizon_hours: int = 168) -> pd.DataFrame:
        """Forecast metric for next horizon_hours (default 1 week)."""
        model = self.models[metric_name]
        future = model.make_future_dataframe(periods=horizon_hours, freq='H')
        future['day_of_week'] = future['ds'].dt.dayofweek
        future['hour'] = future['ds'].dt.hour
        
        forecast = model.predict(future)
        return forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
    
    def detect_anomalies(self, metric_name: str, recent_data: pd.DataFrame) -> pd.DataFrame:
        """Detect anomalies in recent metric data."""
        values = recent_data['value'].values.reshape(-1, 1)
        anomaly_scores = self.anomaly_detector.fit_predict(values)
        recent_data['anomaly_score'] = anomaly_scores
        recent_data['is_anomaly'] = anomaly_scores == -1
        return recent_data
    
    def generate_recommendations(self, forecasts: Dict[str, pd.DataFrame]) -> List[Recommendation]:
        """Generate right-sizing recommendations from forecasts."""
        recommendations = []
        
        for metric, forecast in forecasts.items():
            # Get peak forecast
            peak = forecast['yhat_upper'].max()
            current_limit = self.get_current_limit(metric)
            
            if peak > current_limit * 0.8:
                recommendations.append(Recommendation(
                    type="scale_up",
                    metric=metric,
                    current=current_limit,
                    recommended=peak * 1.2,
                    reason=f"Forecasted peak ({peak:.1f}) exceeds 80% of current limit ({current_limit})",
                    confidence=0.85,
                ))
            
            # Check for over-provisioning
            avg_usage = forecast['yhat'].mean()
            if avg_usage < current_limit * 0.3:
                recommendations.append(Recommendation(
                    type="scale_down",
                    metric=metric,
                    current=current_limit,
                    recommended=max(avg_usage * 2, current_limit * 0.5),
                    reason=f"Average forecasted usage ({avg_usage:.1f}) well below current limit ({current_limit})",
                    confidence=0.75,
                ))
        
        return recommendations

# PrometheusRule for DX metric anomalies
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: dx-metrics-anomalies
  namespace: monitoring
spec:
  groups:
    - name: dx-metrics-anomalies
      interval: 5m
      rules:
        # Scaffold success rate anomaly
        - alert: ScaffoldSuccessRateAnomaly
          expr: |
            (
              rate(platform_scaffold_total{status="success"}[5m]) 
              / 
              rate(platform_scaffold_total[5m])
            ) < 0.95
            and
            rate(platform_scaffold_total[5m]) > 0.1
          for: 5m
          labels:
            severity: warning
            team: platform
          annotations:
            summary: "Scaffold success rate dropped below 95%"
            description: "Success rate: {{ $value | humanizePercentage }}. Check scaffolder logs and GitHub API status."
            runbook_url: "https://runbooks.platform.example.com/scaffold-failures"

        # CI queue time anomaly
        - alert: CIQueueTimeAnomaly
          expr: |
            histogram_quantile(0.95, rate(github_actions_queue_duration_seconds_bucket[5m])) > 600
          for: 10m
          labels:
            severity: warning
            team: platform
          annotations:
            summary: "CI queue time p95 > 10 minutes"
            description: "Queue time p95: {{ $value | humanizeDuration }}. Check runner capacity and queue depth."
            runbook_url: "https://runbooks.platform.example.com/ci-queue"

        # Preview environment failure rate
        - alert: PreviewEnvFailureRateAnomaly
          expr: |
            (
              rate(platform_preview_env_total{status="failed"}[15m])
              /
              rate(platform_preview_env_total[15m])
            ) > 0.1
          for: 5m
          labels:
            severity: warning
            team: platform
          annotations:
            summary: "Preview environment failure rate > 10%"
            description: "Failure rate: {{ $value | humanizePercentage }}. Check ArgoCD sync status and Crossplane claims."
            runbook_url: "https://runbooks.platform.example.com/preview-env-failures"

        # Scaffold-to-deploy time regression
        - alert: ScaffoldToDeployTimeRegression
          expr: |
            histogram_quantile(0.50, rate(platform_scaffold_to_deploy_seconds_bucket[1h])) 
            > 
            1.5 * histogram_quantile(0.50, rate(platform_scaffold_to_deploy_seconds_bucket[1h] offset 7d))
          for: 30m
          labels:
            severity: info
            team: platform
          annotations:
            summary: "Scaffold-to-deploy time increased 50% vs last week"
            description: "Current p50: {{ $value | humanizeDuration }}. Investigate CI/CD pipeline changes."
            runbook_url: "https://runbooks.platform.example.com/scaffold-deploy-regression"

        # Platform API latency anomaly (ML-based)
        - alert: PlatformAPILatencyAnomaly
          expr: |
            histogram_quantile(0.99, rate(platform_api_request_duration_seconds_bucket[5m])) 
            > 
            3 * histogram_quantile(0.99, rate(platform_api_request_duration_seconds_bucket[5m] offset 1h))
          for: 5m
          labels:
            severity: critical
            team: platform
          annotations:
            summary: "Platform API p99 latency 3x higher than 1 hour ago"
            description: "Possible platform API degradation. Check API pods, dependencies, recent deployments."
            runbook_url: "https://runbooks.platform.example.com/api-latency"

import { App, ExpressReceiver } from '@slack/bolt';
import { LLMClient } from './llm';

class PlatformBot {
  private app: App;
  private llm: LLMClient;
  private platformClient: PlatformClient;

  constructor() {
    this.app = new App({
      signingSecret: process.env.SLACK_SIGNING_SECRET,
      token: process.env.SLACK_BOT_TOKEN,
      receiver: new ExpressReceiver({ signingSecret: process.env.SLACK_SIGNING_SECRET }),
    });
    this.llm = new LLMClient();
    this.platformClient = new PlatformClient();
    this.registerHandlers();
  }

  private registerHandlers() {
    // Mention handler
    this.app.event('app_mention', async ({ event, say }) => {
      const text = event.text.replace(/<@[A-Z0-9]+>/, '').trim();
      const response = await this.processQuery(event.user, text);
      await say({ text: response, thread_ts: event.ts });
    });

    // Slash commands
    this.app.command('/platform', async ({ command, ack, respond }) => {
      await ack();
      const response = await this.processQuery(command.user_id, command.text);
      await respond(response);
    });

    // Interactive components (buttons, selects)
    this.app.action('platform_action', async ({ action, ack, respond }) => {
      await ack();
      const response = await this.handleAction(action);
      await respond(response);
    });
  }

  private async processQuery(userId: string, query: string): Promise<string> {
    // Get context: user's team, recent activity, open PRs
    const context = await this.getUserContext(userId);

    const prompt = `
You are the Platform Assistant. Help developers with platform questions.

User: ${context.userName} (Team: ${context.team})
Recent activity: ${context.recentActivity}
Open PRs: ${context.openPRs}

Available tools:
- get_service_info(service_name)
- get_deployment_status(service_name)
- get_logs(service_name, since)
- get_metrics(service_name, metric, duration)
- search_docs(query)
- search_runbooks(query)
- create_scaffold_plan(description)
- check_policy_compliance(service_name)

User question: "${query}"

If you need to use a tool, respond with:
TOOL: tool_name(arguments)

If you can answer directly, provide a helpful response.
`;

    let response = await this.llm.complete({ prompt, temperature: 0.1 });

    // Handle tool calls
    while (response.includes('TOOL:')) {
      const toolMatch = response.match(/TOOL: (w+)((.*))/);
      if (!toolMatch) break;

      const [, toolName, argsStr] = toolMatch;
      const args = JSON.parse(argsStr);
      let toolResult;

      switch (toolName) {
        case 'get_service_info':
          toolResult = await this.platformClient.getService(args.service_name);
          break;
        case 'get_deployment_status':
          toolResult = await this.platformClient.getDeploymentStatus(args.service_name);
          break;
        case 'search_docs':
          toolResult = await this.platformClient.searchDocs(args.query);
          break;
        // ... other tools
      }

      response = await this.llm.complete({
        prompt: `${prompt}

Tool result: ${JSON.stringify(toolResult)}

Continue answering.`,
        temperature: 0.1,
      });
    }

    return response;
  }
}