AI agent products need more than code generation
Building a real AI agent product takes much more than writing the main feature. Tools like Claude Code or Codex can handle the core logic when the instructions are clear.
The harder work comes from product and choices: how users log in, whether to support Google or GitHub login, where the needed API keys come from, and whether to pay for a service such as Clerk just for . Deployment also adds decisions, such as whether to use AWS, Vercel, or another host, and whether the product must handle small or large traffic.
Giving AWS keys to an agent should use , but fatigue can make people skip that care. If users can run AI features inside the product, there are more choices around using one shared API key, provider , and whether each request needs a sandbox.
Key points
- code may be only a small part of building an AI agent product.
- Claude Code and Codex can help with logic when the builder gives clear direction.
- Login choices create extra work around Google, GitHub, API keys, and paid tools like Clerk.
- Cloud keys should have before an agent uses them.
- AI features for users raise questions about shared API keys, , and sandbox costs.