# What a genuine handover looks like when the client uses AI coding tools.

> The ability to edit source code is useful only when the codebase’s constraints and dependencies are understandable.

Canonical URL: https://marmation.com/review/client-handover-for-ai-native-codebases/

By Vikram Roy · 2026-11-12

A client commissions a custom application and plans to maintain it with its own engineers using AI coding agents. The supplier transfers a repository and declares the work complete. The next engineer discovers undocumented environment variables, external services, model assumptions and fragile deployment steps.

## The decision behind the technology

The modern handover cannot stop at access. Generative tooling increases the ease of making changes, which makes architectural context, tests and operational documentation more important. A coding agent needs reliable constraints rather than a repository of unknown conventions.

## A better way to frame the work

Provide architecture decision records, system/data flow, dependency and license register, environment setup, deployment runbook, tests, evaluation cases, permissions matrix, known risks and acceptance history. Validate the handover with a second person actually building and releasing a bounded change.

## Put the idea under realistic pressure

Ask a new engineer to recreate an environment, run tests, change a nontrivial rule and prove behavior still holds. Record every undocumented step. That exercise is a better measure of independence than merely transferring the GitHub organization.

## When the recommendation changes

A client may legitimately retain a managed engineering partner. Ownership does not require every company to staff its own technical operations. It does require the option to change provider without unnecessary lock-in, subject to the contract.

## What to decide before moving forward

Define what custom IP transfers, what remains licensed, what documentation is included and how access is managed after acceptance. Treat handover as a testable milestone.

### References and context

[Stack Overflow Developer Survey 2026](<https://survey.stackoverflow.co/2026/ai/data>); [NIST AI RMF](<https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence>).

## Sources

- [Generative AI Profile for AI RMF](<https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence>) — NIST. Reference governance and risk design principles for gen AI. Risk management reference for generative AI Limit: Not legal certification, nor proof of Marmation compliance. Not certification of Marmation practices
- [Developer Survey 2026: AI](<https://survey.stackoverflow.co/2026/ai/data>) — Stack Overflow. Coding agents and developer workflows adoption context. Limit: Responses are a developer survey, not entire engineering workforce.

## Related reading

- [The choices behind technology investments](https://marmation.com/review/decisions/)
- [Build, buy, extend or integrate? The AI decision that comes before the technology.](https://marmation.com/review/build-buy-extend-or-integrate/)
- [How to evaluate an AI engineering partner.](https://marmation.com/review/choosing-an-ai-engineering-partner/)