AI-Native Codebase

The Challenge

A senior developer responsible for an important code repository was planning to leave a company in the DevOps SaaS space. With no replacement in sight to maintain the code, the leadership team were hoping for an AI development automation solution to the problem.

Our Solution

We connected an AI agent to analyze the repository and produce comprehensive documentation and instructions for AI-driven code maintenance.

We generated:

• Complete context for the codebase, including original business requirements, current state, and future plans
• A full suite of unit and integration tests to serve as a benchmark for the coding agent
• A language-agnostic coding agent setup which, when prompted, can rewrite the repository from scratch in any suitable programming language (tested with Python and TypeScript)
• A system of sanity checks that allows the coding agent to make modifications and add new features while maintaining backward compatibility and fidelity to the original purpose of the codebase

Code Transformation

The AI agent transforms cryptic legacy code into clean, maintainable modules:

def proc(d, t):
    r = []
    for i in d:
        if i['s'] == t:
            x = i['v'] * 1.1
            r.append(x)
    return sum(r) / len(r) if r else 0

"""
Calculate average adjusted price for a tier.
Business Rule: 10% markup applied per item.
"""
def calculate_tier_average(
    items: list[dict],
    tier: str
) -> float:
    # Filter and apply markup
    prices = [
        item['value'] * MARKUP_RATE
        for item in items
        if item['status'] == tier
    ]
    return mean(prices) if prices else 0

The Impact

• The code is now easily maintainable by a single senior developer or even team lead/CTO
• 90% reduction in feature delivery time
• Reduction in one $100k+ per year position
• Product team members can now contribute features with minimal technical oversight