ai-orchestrator.config.yaml

e latency, and fallback frequency. Enforce per-team and per-request cost limits with automatic degradation to cheaper models when thresholds are breached. 4. Add Security & Compliance Gate: Pre-scan AI-generated code for secrets, unsafe patterns, and license violations. Post-process outputs to enforce formatting standards and add telemetry headers. 5. Deploy as IDE Extension + CI/CD Plugin: Expose the orchestrator via language server protocol (LSP) for IDEs and as a GitHub/GitLab action for pipeline integration. Ensure zero-config fallback to direct tool access if the orchestrator fails.

Code Example (TypeScript)

import { createHash } from 'crypto';
import { z } from 'zod';

// ─── Types & Interfaces ─────────────────────────────────────────────────────
interface AIProvider {
  id: string;
  name: string;
  contextWindow: number;
  costPer1kTokens: number;
  maxLatencyMs: number;
  capabilities: string[];
  route(request: AIRequest): Promise<AIResponse>;
}

interface AIRequest {
  prompt: string;
  language: string;
  fileSize: number;
  securityLevel: 'low' | 'medium' | 'high';
  metadata: Record<string, string>;
}

interface AIResponse {
  content: string;
  providerId: string;
  tokensUsed: number;
  latencyMs: number;
  cost: number;
  fallbackUsed: boolean;
}

// ─── Policy Engine ──────────────────────────────────────────────────────────
const PolicySchema = z.object({
  maxCostPerRequest: z.number().positive(),
  maxCostPerDay: z.number().positive(),
  blockedProviders: z.array(z.string()).optional(),
  requireSecurityScan: z.boolean().default(true),
});

type Policy = z.infer<typeof PolicySchema>;

// ─── Telemetry Collector ────────────────────────────────────────────────────
class TelemetryStore {
  private dailyCosts: Map<string, number> = new Map();
  private requestLog: AIResponse[] = [];

  trackCost(teamId: string, cost: number): void {
    this.dailyCosts.set(teamId, (this.dailyCosts.get(teamId) || 0) + cost);
  }

  getDailyCost(teamId: string): number {
    return this.dailyCosts.get(teamId) || 0;
  }

  logResponse(response: AIResponse): void {
    this.requestLog.push(response);
  }

  getAverageLatency(): number {
    if (this.requestLog.length === 0) return 0;
    return this.requestLog.reduce((sum, r) => sum + r.latencyMs, 0) / this.requestLog.length;
  }
}

// ─── Orchestrator Core ──────────────────────────────────────────────────────
export class AIDevToolOrchestrator {
  private providers: Map<string, AIProvider> = new Map();
  private telemetry: TelemetryStore = new TelemetryStore();
  private policy: Policy;

  constructor(policy: Partial<Policy>) {
    this.policy = PolicySchema.parse(policy);
  }

  registerProvider(provider: AIProvider): void {
    this.providers.set(provider.id, provider);
  }

  async route(request: AIRequest, teamId: string): Promise<AIResponse> {
    // 1. Policy enforcement
    if (this.telemetry.getDailyCost(teamId) >= this.policy.maxCostPerDay) {
      throw new Error('Daily cost limit exceeded for team');
    }

    // 2. Provider selection strategy
    const eligible = Array.from(this.providers.values()).filter(p => {
      if (this.policy.blockedProviders?.includes(p.id)) return false;
      if (request.securityLevel === 'high' && !p.capabilities.includes('security-hardened')) return false;
      if (request.fileSize > p.contextWindow * 0.8) return false; // Reserve 20% for response
      return true;
    });

    if (eligible.length === 0) {
      throw new Error('No eligible providers for request constraints');
    }

    // Sort by cost efficiency + latency score
    const sorted = eligible.sort((a, b) => {
      const scoreA = (a.costPer1kTokens / 1000) * request.prompt.length + (a.maxLatencyMs * 0.1);
      const scoreB = (b.costPer1kTokens / 1000) * request.prompt.length + (b.maxLatencyMs * 0.1);
      return scoreA - scoreB;
    });

    let response: AIResponse | null = null;
    let fallbackUsed = false;

    // 3. Execute with fallback chain
    for (const provider of sorted) {
      try {
        const start = performance.now();
        const raw = await provider.route(request);
        const latency = performance.now() - start;

        response = {
          content: raw.content,
          providerId: provider.id,
          tokensUsed: raw.tokensUsed,
          latencyMs: latency,
          cost: (raw.tokensUsed / 1000) * provider.costPer1kTokens,
          fallbackUsed: false,
        };

        if (response.cost > this.policy.maxCostPerRequest) {
          throw new Error('Request cost exceeds policy limit');
        }
        break;
      } catch (err) {
        console.warn(`Provider ${provider.id} failed or exceeded limits:`, err);
        fallbackUsed = true;
        continue;
      }
    }

    if (!response) {
      throw new Error('All providers failed or violated policies');
    }

    response.fallbackUsed = fallbackUsed;
    this.telemetry.trackCost(teamId, response.cost);
    this.telemetry.logResponse(response);

    // 4. Security scan (if enabled)
    if (this.policy.requireSecurityScan) {
      await this.scanForSecrets(response.content);
    }

    return response;
  }

  private async scanForSecrets(content: string): Promise<void> {
    const secretPatterns = [
      /AKIA[0-9A-Z]{16}/, // AWS
      /ghp_[a-zA-Z0-9]{36}/, // GitHub
      /sk-[a-zA-Z0-9]{20,}/, // Generic API keys
    ];
    const found = secretPatterns.some(pattern => pattern.test(content));
    if (found) {
      throw new Error('AI output contains potential secrets. Blocked by policy.');
    }
  }

  getTelemetry() {
    return {
      dailyCosts: Object.fromEntries(this.telemetry.dailyCosts),
      avgLatency: this.telemetry.getAverageLatency(),
      totalRequests: this.telemetry.requestLog.length,
    };
  }
}

Architecture Decisions & Rationale

The orchestrator uses a strategy pattern for provider routing to avoid hardcoding vendor logic. This enables hot-swapping models as the market evolves without refactoring workflow code. The policy engine is decoupled from routing logic, allowing security and finance teams to update constraints independently. Telemetry is aggregated in-memory for low-latency enforcement, with async export to external monitoring systems (Prometheus, Datadog) recommended for production.

Context window management is handled conservatively: the router reserves 20% of the window for response generation to prevent truncation. Fallback chains prioritize cost efficiency over raw capability, reflecting production reality where predictable latency and budget adherence outweigh marginal quality improvements. The security gate runs post-generation because AI models frequently output placeholder keys or example credentials that must be caught before commit.

This architecture treats AI tools as network services rather than IDE plugins. It introduces service discovery, rate limiting, circuit breaking, and observability—the same patterns used for microservices. Engineering teams gain predictable costs, consistent output quality, and audit trails without sacrificing developer velocity.

Pitfall Guide

1. Assuming Context Windows Solve All Problems: Larger context windows don't eliminate fragmentation. When multiple tools load overlapping files, token duplication inflates costs and degrades model attention. Best practice: implement a shared context cache that deduplicates file contents and indexes only changed regions.

2. Skipping Pre-Commit Security Scans for AI Code: AI models generate syntactically correct but insecure patterns (hardcoded secrets, unsafe deserialization, overly permissive CORS). Best practice: enforce mandatory static analysis and secret detection on all AI-generated diffs before merge.

3. Chasing Model Releases Without Benchmarking: New model versions often optimize for general benchmarks, not specific codebases. Blindly upgrading causes regression in domain-specific tasks. Best practice: maintain a golden dataset of team-specific prompts and run automated regression tests before switching providers.

4. No Cost or Usage Caps: Token spend scales non-linearly with team size and IDE activity. Without per-request and daily limits, budgets blow up during sprint crunches. Best practice: implement tiered limits (individual, team, org) with automatic fallback to cheaper models when thresholds are approached.

5. Treating AI as Code Replacement Instead of Workflow Multiplier: AI excels at boilerplate, documentation, and refactoring, but struggles with architectural decisions and cross-module integration. Best practice: define clear boundaries. Use AI for generation, humans for design and review. Measure delta, not absolute speed.

6. Ignoring IDE vs CI/CD Context Differences: IDE extensions operate with live file state; CI/CD pipelines run in isolated environments. Outputs that work locally often fail in pipelines due to missing dependencies or environment variables. Best practice: standardize prompt templates with explicit environment declarations and run AI outputs through the same build/test matrix as human code.

7. Over-Engineering the Control Plane: Building a monolithic orchestrator that tries to replace IDE features causes latency and adoption resistance. Best practice: keep the orchestrator stateless and lightweight. Delegate UI/UX to existing extensions, and use the orchestrator purely for routing, policy, and telemetry.

Production Bundle

Action Checklist

• Audit current AI tool inventory: document every extension, plugin, and internal agent in use across IDEs and CI/CD.
• Define capability matrix: map each tool to language support, context limits, latency profile, and security posture.
• Implement cost governance: set per-request and daily caps with automatic fallback to budget-optimized models.
• Deploy security gate: integrate secret detection and pattern scanning into the post-generation pipeline.
• Standardize telemetry: track tokens, latency, fallback frequency, and cost per feature across all teams.
• Establish regression benchmarks: maintain a dataset of team-specific prompts and run automated validation before provider changes.
• Document usage boundaries: publish clear guidelines on when to use AI for generation vs. when human design is required.

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
Startup (<20 engineers)	Single vendor + lightweight router	Minimizes integration overhead while establishing governance early	Low ($200-500/mo)
Mid-size (20-100 engineers)	Unified orchestrator with 3-4 providers	Balances capability diversity with cost control and compliance	Medium ($1,200-3,000/mo)
Enterprise (>100 engineers)	Federated control plane + policy-as-code	Enables team autonomy while enforcing org-wide security and budget limits	High ($4,000-8,000/mo)
Regulated industry (FinTech/Health)	On-prem models + strict policy gates	Meets data residency and audit requirements without sacrificing velocity	Premium (infrastructure heavy)

Configuration Template

# ai-orchestrator.config.yaml
policy:
  maxCostPerRequest: 0.15
  maxCostPerDay: 45.00
  blockedProviders: []
  requireSecurityScan: true
  fallbackStrategy: cost-optimized

providers:
  - id: copilot-enterprise
    name: "GitHub Copilot Enterprise"
    contextWindow: 100000
    costPer1kTokens: 0.018
    maxLatencyMs: 800
    capabilities: ["security-hardened", "typescript", "react"]
    endpoint: "https://api.githubcopilot.com/v1/chat"

  - id: claude-sonnet
    name: "Anthropic Claude Sonnet"
    contextWindow: 200000
    costPer1kTokens: 0.012
    maxLatencyMs: 1200
    capabilities: ["long-context", "refactoring", "documentation"]
    endpoint: "https://api.anthropic.com/v1/messages"

  - id: internal-codellama
    name: "Internal Fine-Tuned CodeLlama"
    contextWindow: 8000
    costPer1kTokens: 0.002
    maxLatencyMs: 500
    capabilities: ["boilerplate", "testing", "budget-optimized"]
    endpoint: "http://internal-ai-gateway:8080/generate"

telemetry:
  exportInterval: 30s
  metricsEndpoint: "http://monitoring.internal:9090/metrics"
  retentionDays: 90

security:
  secretPatterns:
    - "AKIA[0-9A-Z]{16}"
    - "ghp_[a-zA-Z0-9]{36}"
    - "sk-[a-zA-Z0-9]{20,}"
  blockOnMatch: true
  scanTimeoutMs: 2000

Quick Start Guide

1. Install the orchestrator: npm install @codcompass/ai-dev-orchestrator and create ai-orchestrator.config.yaml using the template above.
2. Register providers: Add your API keys to environment variables, then run npx ai-orchestrator init to validate connectivity and capability mapping.
3. Integrate with IDE: Install the provided VS Code/JetBrains extension, point it to http://localhost:3000/api/v1/route, and verify telemetry streaming to your monitoring endpoint.
4. Enforce policies: Set maxCostPerRequest and requireSecurityScan: true, then run npx ai-orchestrator test --dataset=./benchmarks to validate fallback chains and compliance gates.
5. Deploy to CI/CD: Add the GitHub/GitLab action step uses: codcompass/ai-orchestrator-action@v1 to your pipeline, configure team IDs, and monitor cost/latency dashboards within 5 minutes.