Google has officially introduced Conductor, an open-source extension for the Gemini CLI designed to transition AI-assisted programming from ephemeral chat sessions to a structured, context-driven development model. By storing technical decisions, product requirements, and execution plans as versioned Markdown files directly within a repository, Conductor ensures that AI agents operate with persistent knowledge rather than isolated, ad hoc prompts.

From Ephemeral Prompts to Persistent Context

Traditional AI coding often suffers from “session drift,” where context is lost once a chat window is closed. Conductor addresses this by maintaining a dedicated directory within the project repository. This directory serves as a centralized source of truth, capturing essential project elements such as:

• Product goals and user constraints.

• Technical stack specifications and style guides.

• Standardized workflow rules and testing expectations.

By integrating these artifacts into the version control system, AI behavior remains consistent across different environments and team members, providing a reviewable and auditable trail of technical intent.

The Engineering Lifecycle and Implementation

The extension enforces a rigorous three-stage lifecycle: Context, Spec and Plan, and Implementation. Rather than generating code immediately from a natural language request, Conductor requires the creation of “tracks”—dedicated units of work that formalize requirements before any execution begins.

For each track, the system generates a spec.md to define requirements and a plan.md to outline a stepwise execution strategy.

Version-Controlled Agent Orchestration

Once a developer approves the generated plan, the /conductor:implement command hands control to the agent. The agent iteratively inspects relevant files, proposes code changes, and runs validation checks defined in the project’s workflow guidelines. High-level checkpoints are built into the process, ensuring human oversight and preventing unreviewed refactors.

This framework is highly effective for both greenfield and brownfield projects. In existing codebases, the setup process extracts implicit system knowledge into explicit documentation. By leveraging Git for rollbacks and reviews, Conductor bridges the gap between autonomous AI capabilities and professional software engineering standards.