Technical Strategy With AI

TL;DR

• Define the "Approved AI Stack" (IDE, Agent, Gateway). If you don't, every dev picks their own—shadow AI, security leaks, and chaos.
• If AI writes code 2x faster, the bottleneck shifts to Product Definition and QA. Plan for it. Invest in automated QA and observability.
• Strategic pillars for 2026: AI Gateway, Agent Architecture, and managing the throughput explosion.

Technical strategy used to be cloud + language + database. Now it's cloud + stack + AI models + agent framework + governance. The variables multiplied. Your job is to control them.

The Approved AI Stack

IDE and coding assist. Cursor, GitHub Copilot Enterprise, or something else. Pick one (or a short approved list). Document it. Provide enterprise licenses. If you don't, devs will use random web UIs and paste code into ChatGPT. That's shadow AI. That's IP leakage. Standardization isn't bureaucracy—it's risk management.

Agent framework. LangGraph, CrewAI, LangChain, Vercel AI SDK. Pick a standard. Don't let Team A use LangGraph and Team B use AutoGen unless there's a reason. Agent sprawl creates maintenance hell. One framework. Document it in your tech radar or ADRs. Share with all teams.

AI Gateway. Don't let every app call OpenAI directly. Route through a gateway (Portkey, Helicone). Why? Caching (save money), observability (see what's being sent), and model swapping (switch from GPT-5.2 to Claude Sonnet 4.6 instantly). One policy: route at least one workload through a gateway. Use it as the template for rollout.

Managing Shadow AI Risk

The problem. Devs are using AI tools. Do you know which ones? Code leaking to training data. API keys in prompts. Unapproved vendors. If you don't provide a safe way, they'll use an unsafe way. That's not negligence—it's human nature. Remove the friction for safe tools; you reduce the incentive for unsafe ones.

The fix. Provide enterprise licenses for the best tools. Audit your AI bill. Who pays for what? Cursor, Copilot, API gateways, model APIs? Centralize visibility. If spend is distributed across 10 cost centers, you're blind. One place. One policy. Communicate it.

The New Bottleneck: PRs Drown You

The math. If AI writes code 2x faster, you get 2x more PRs. Same reviewers. Same QA capacity. Something breaks. Historically the bottleneck was "get code written." Now it's "get code reviewed and tested." If you don't plan for that, you'll drown in merge conflicts and broken builds.

The strategy. Invest heavily in automated QA. Invest in observability. Hire for architects and reviewers, not just coders. The bottleneck moved. Your org structure and tooling need to move with it. Don't add more writers. Add more reviewers. Add more quality gates. Add more visibility into what's actually running.

Strategic Pillars for 2026

1. The AI Gateway

Route all AI workloads through a gateway. Measure latency, cost, and usage. Swap models without changing app code. One place to enforce rate limits, audit logs, and data policies. Start with one workload. Prove it. Expand.

2. Agent Architecture

Decide on your agent framework. Python: LangGraph or CrewAI. TypeScript: LangChain.js or Vercel AI SDK. Standardize. Don't let every team invent their own. Agent code is code. It needs architecture, review, and maintenance.

3. Throughput and Quality

AI accelerates output. Output without quality is technical debt. Prioritize: automated tests, CI/CD gates, deployment confidence. The teams that scale with AI are the ones that invested in quality first. The ones that didn't are debugging in production.

Do This Next

1. Audit your AI bill. Who is paying for what? Centralize visibility. If spend is distributed, you're blind.
2. Document your Approved AI Stack. IDE, Agent framework, Gateway. Add to tech radar or ADRs. Share with all teams—no shadow AI.
3. Model the bottleneck. If dev velocity doubles, where do PRs and QA break? Invest there. Automated QA, observability, or reviewer capacity.