🛠️🧭 Principles for AI Coding → How to Build Production AI Agents with Claude.
𝗠𝘆 𝟳-𝗦𝘁𝗲𝗽 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗲𝘀𝘁𝗮𝗯𝗹𝗲, 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗮𝗻𝗱 𝘀𝗮𝗳𝗲-𝘁𝗼-𝗰𝗵𝗮𝗻𝗴𝗲 𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗰𝗼𝗱𝗲.
Most AI engineers are hearing:
“Code is cheap.”
I think this is dangerously wrong.
Bad code is now more expensive because AI can multiply it faster.
My 7-Step roadmap for using AI coding agents like a pro:
》𝗦𝘁𝗲𝗽 𝟭: Turn the Prompt into a Design Mission
✸ Define the goal, constraints, risk, and acceptance criteria.
✸ Tell the agent what “done” means before it starts.
✸ Do not start from a vague request.
→ Example: “Interview me first. Find the failure paths.”
》𝗦𝘁𝗲𝗽 𝟮: Build a Shared Design Concept
✸ Make the agent ask questions before planning.
✸ Resolve unclear decisions early.
✸ Do not confuse a generated plan with understanding.
→ Example: “What can break before we write the PRD?”
》𝗦𝘁𝗲𝗽 𝟯: Create a Shared Language
✸ Define domain terms, modules, actions, and data names.
✸ Use the same words in prompts, code, tests, and docs.
✸ Do not let the agent rename concepts every sprint.
→ Example: “PatientRiskScore” means one thing everywhere.
》𝗦𝘁𝗲𝗽 𝟰: Give the Agent a Module Map
✸ Show where the change belongs.
✸ Name the module, interface, dependencies, and boundaries.
✸ Do not let the agent wander through the repo.
→ Example: Retry logic belongs behind PaymentGateway.
》𝗦𝘁𝗲𝗽 𝟱: Force Small Feedback Loops
✸ Use tests, types, linting, logs, and browser checks.
✸ Make the agent check after small changes.
✸ Do not let it write 800 lines before testing.
→ Example: Red test, code, type check, green test, refactor.
》𝗦𝘁𝗲𝗽 𝟲: Design Deep Modules
✸ Hide complexity behind simple interfaces.
✸ Build fewer, stronger boundaries.
✸ Do not create 40 tiny helper files.
→ Example: One ingestion service hides parsing, chunking, storage, and errors.
》𝗦𝘁𝗲𝗽 𝟳: Own the Interface, Verify the Code
✸ You design contracts, schemas, methods, and review points.
✸ Let the agent implement low-risk internals.
✸ Check tests, logs, affected modules, and regression risk.
→ Example: Approve the tool schema before Claude writes the handler.
𝗣𝗹𝗲𝗮𝘀𝗲 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿:
Let Claude write the code, but do not let it design the system alone.
Before you ship, check the interface, the tests, the failure paths, and the modules it touched.
That is where AI engineers stand out now.
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ALT Infographic titled “How to Build Production AI Agents with Claude Code Using Software Principles” by Dr. Maryam Miradi. The visual presents a 7-step framework for building production-ready AI agents: turn the prompt into a design mission, build a shared design concept, create a shared language, give the agent a module map, force small feedback loops, design deep modules, and own the interface while verifying the code. The roadmap emphasizes planning before coding, shared understanding, modular architecture, frequent testing, clear interfaces, and safe change management. The outcome is AI-generated software that is testable, modular, safe to change, and built to scale.