AI Coding Agent / CodeComply Concept

Problem

AI coding assistants are productive but often suggest code that quietly violates enterprise conventions — logging rules, authorization patterns, dependency policies, and review checklists.

Business context

An internal developer-experience concept where AI suggestions must be filtered through the same conventions a senior reviewer would apply.

My role

Concept owner: rules surface, agent loop, suggestion review UX, and integration story with existing review tooling.

Solution

• Defined a rules surface where teams can encode conventions as plain-language checks plus optional code probes.
• Wrapped the AI agent loop so each suggestion is critiqued against the rules before it lands in the editor.
• Surfaced rule violations inline with the suggestion, with a one-line explanation and a link to the convention.
• Logged every accepted suggestion for later review and rule tuning.

Architecture highlights

• Editor-side extension talks to a small service that wraps the LLM call.
• Rule packs live in a versioned repository and are pulled per team.
• Audit log records prompt, response, rule outcomes, and final acceptance.

Challenges

• Keeping latency low enough that the agent feels like an assistant, not a gate.
• Avoiding rule sprawl — every team wants their own check.
• Designing graceful fallbacks when the model is unavailable.

Outcome / impact

• Improved maintainability by catching convention drift at suggestion time.
• Improved delivery confidence for teams adopting AI coding assistants.
• Reduced manual review effort for common style and policy checks.

What I learned

• An AI assistant is most valuable when it encodes the senior engineer's review checklist.
• Rules belong in code, not in chat history.