Existing repositories

Use Autonomous AI Engineering on an Existing Codebase

Can autonomous AI engineering work on an existing codebase?

Yes, when agents first map the repository and its architecture, then make bounded changes against an exact baseline with meaningful regression gates. Nexus preserves the discovery record, work boundaries, evidence and integration authority needed to change an existing codebase without treating it as a blank-slate generation task.

Discovery comes before implementation

An existing repository contains conventions, dependencies, migration history and operational assumptions that a new feature brief may not mention. Nexus can retain architecture mapping and project decisions as durable context for later work.

The aim is not to ingest everything into one prompt. It is to identify the relevant subsystem, ownership boundary and verification path for each proposed change.

Repository and build discovery
Architecture and dependency mapping
Existing conventions
Risk and regression boundaries

Bound the change and its proof

An AI coding agent working on an existing repository should receive an exact baseline, explicit acceptance criteria and a narrow work fence. Tests and build checks should cover the affected behaviour, not merely prove that the agent could run a command.

Independent review considers the diff in the context of the existing architecture. If assumptions are wrong, findings return to remediation with the original evidence preserved.

Integrate into one canonical history

Parallel branches remain candidates until the governed integration path accepts them. Nexus separates producing a commit from authorising it to become canonical.

This supports legacy and current systems alike: customer-owned Git history remains portable, and human teams can inspect or continue the work without dependence on an agent conversation.

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