When Should a Business Build a Custom AI Agent?

Overview

Custom AI agents become valuable when the workflow is repetitive, connected to real tools, and needs decision support or action.

This guide documents the approach NorthFlow Studio uses when building AI Agents for client and internal projects. It is written to be useful on its own — copy the patterns, adapt them, and test them in your environment.

Approach

We start from the constraint: what must be true for this system to be reliable, secure and cheap to operate? From there we choose the simplest structure that meets those constraints, then add complexity only when measured data justifies it.

Structure

src/
  routes/        # entrypoints
  domain/        # business logic, framework-free
  infra/         # db, auth, external clients
  test/          # unit + integration

Deployment

Ship behind a CI pipeline with type checks and tests. Use environment-scoped configuration and migrations for any schema change. Roll out behind a health check.

Security

• Validate and sanitize all external input.
• Scope credentials to least privilege.
• Audit the actions that matter and log them durably.

FAQ

See the FAQ section above. For implementation help, book a strategy call.