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Genesis Code Protocol – Project Roadmap

This roadmap outlines the planned evolution of the Genesis Code Protocol (GCP) beyond the V50 flagship release. It translates the community’s high‑level goals into concrete milestones. Each milestone builds upon the existing protocol while preserving backward compatibility and the core principles of transparency, auditability and openness.

Mission

Create and refine a comprehensive, open‑source protocol for systematic, AI‑driven invention. Make the innovation process transparent, auditable and accessible to anyone with a capable language model.

Overview

The GCP evolves through numbered editions. Version 50 introduced autonomous spark generation, unified bootstrapping and extended phase maps encompassing data rights and compliance【357218564026002†L0-L71】. Future versions focus on making the AI’s work more verifiable, the human experience more seamless and the orchestration more intelligent. This roadmap treats upcoming releases as incremental refinements rather than complete rewrites.

Milestone 1: V50.1 — Foundation & Usability (Current Focus)

Theme: Solidify the V50 foundation and improve the core chat‑based user experience. This milestone emphasises documentation, examples and human‑in‑the‑loop interactions.

Key Goals:

  • Finalize V50 runners and cartridges. Ensure all runners defined in the Master Runners Codex and cartridges in the Cartridges Pack are fully documented with examples. Provide usage guides and cross‑link them in the wiki.
  • Interactive gate UX. Refine the Gate Decision Card UX to be more interactive in chat, allowing users to approve/reject or select options with simple, button‑like replies. This may involve embedding structured UI elements (e.g. Quick Reply buttons) in chat platforms.
  • Quick Start guides. Develop concise “Getting Started” guides for GCP V50 and its supplements. Walk new users through launching a session, generating sparks, attaching runners and interpreting gate cards.
  • Human collaboration best practices. Create a guide for effectively using the RUNNER_HITL_CONSULT and other human‑in‑the‑loop runners. Include sample prompts for soliciting creative, ethical and strategic feedback from the AI.
  • Accessibility & language support. Begin drafting guidelines for translating key documents and prompts into other languages to broaden adoption.

Milestone 2: V51.0 — Real‑Time AI Verification (“Validator” Release)

Theme: Introduce the first generation of AI‑driven, real‑time verification capabilities. Move from planning tests to executing them, enabling the protocol to validate its own outputs and raise confidence in the generated artifacts.

Key Goals:

  • Tool execution engine. Develop a secure, sandboxed environment where the AI agent can execute a whitelisted set of command‑line tools (e.g. Python interpreters, linters, compilers, TLA+ model checkers). Each tool must have a deterministic contract and be isolated from external networks to protect user data and maintain reproducibility.
  • New RUNNER_VALIDATOR. Design a runner that accepts a test plan (e.g. design/test_matrix.md) and prototype code, runs the specified tests in the sandbox and generates a Validation_Report.md with pass/fail results, coverage metrics and performance benchmarks.
  • Runner enhancements. Update existing runners to take advantage of the validator:
  • The P‑5 Prototype & Tooling phase should not only generate unit tests but also execute them via the validator and record results.
  • The V‑4 Formal Specification & Verification phase should attempt to run the TLA+ model checker against generated specs and report the output.
  • Verification metrics. Define metrics for measuring the efficacy of the validator (e.g. test coverage, code quality improvements, reduced regressions) and incorporate them into the acceptance criteria for future phases.

Milestone 3: V51.1 — Intelligent Orchestrator

Theme: Make the AI a smarter and more autonomous orchestrator of its own process. Improve efficiency and user situational awareness by equipping the model with proactive capabilities and visual aids.

Key Goals:

  • Proactive assistance. Enhance the AI’s ability to analyse the run context and proactively offer suggestions. For example, it could recommend attaching a specific cartridge mid‑run if it detects a shift in the project’s focus, or warn the user if a design path is likely to fail a future compliance gate.
  • Sophisticated orchestration. Evolve the O‑10 Orchestration phase by incorporating predictive models for cost, time and carbon usage. Use these predictions to adjust plans dynamically and surface trade‑offs to the user.
  • RUNNER_VISUALIZER. Create a runner that takes structured artifacts (e.g. plan/sequence.json, ClaimGraph.json) and generates visual diagrams using Mermaid.js or a similar syntax. These diagrams provide an easy‑to‑understand “map” of the process, enhancing the user experience.
  • Context summarisation. Develop features for summarising the current state of a run—including active phases, attached modules, costs and pending decisions—into a concise status report. Present this report on demand or at key checkpoints.

Future Vision (Post‑V51)

  • Multi‑agent collaboration. Explore architectures in which multiple, specialised AI agents run different runners in parallel. An RUNNER_ORCHESTRATOR would coordinate their work, manage dependencies and merge outputs, enabling faster and more complex projects.
  • Self‑healing protocol. Implement a feedback loop where insights from the RUNNER_POST_MARKET and other post‑deployment phases automatically propose and draft refinements to the protocol itself. The AI could identify recurring pain points, update templates and suggest improvements to runners or cartridges.
  • Extended compliance & ethics gates. Incorporate new regulations (e.g. emerging AI legislation) and ethical frameworks into the protocol. Develop runners and cartridges that help users navigate these requirements.
  • Community contributions. Foster a plug‑in ecosystem around the GCP, making it easy to share new runners, cartridges, test suites and visualisation tools. Encourage academic and industry partners to contribute domain expertise.

This roadmap is aspirational. Milestones and goals may evolve as AI capabilities advance and community needs change. Your contributions and feedback are welcome as we continue to make the Genesis Code Protocol a trusted framework for AI‑assisted innovation.