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Roadmap & proposals

Design thinking for AgentOS lives in a design-thinking pipeline, not in scattered GitHub issues. Every non-trivial change flows through the same four artifacts.

Each project is a folder with files in pipeline order:

pain.md What's broken and why it matters
proposal.md The plan (required: prior art / web research)
review.md Adversarial review (one or more rounds: v1, v2, …)
closeout.md What actually shipped vs. what was proposed

No numeric prefixes — files sort alphabetically, and that matches the pipeline order well enough.

_roadmap/
p1/ Priority 1 — active
p2/ Priority 2 — queued
p3/ Priority 3 — backlog
_archive/ Completed projects (historical record)
_drafts/ Rejected proposals (kept for rationale)
_research/ Background research informing proposals

The priority is the parent folder — moving a project between p2/ and p1/ is how prioritization is recorded. Completed projects graduate to _archive/. Rejected proposals go to _drafts/ (the rationale is often more useful than the proposal itself).

Engine repos (core, skills, site, apps) stay focused on code and the ontology. Roadmap material is design thinking — reasoning about architecture, not the architecture itself. Mixing them pollutes git history with churn that has no bearing on the engine and leaks half-formed ideas into the public surface.

_roadmap/ is private. Nothing there is API.

Proposals get adversarial reviews — an agent (or Joe) reads the proposal and tries to break it. Typical review output:

  • Is the pain real and clearly articulated?
  • Does the proposal actually solve it, or just the symptoms?
  • What prior art was missed?
  • What’s the weakest link? What assumption, if wrong, collapses the plan?
  • Where are the escape hatches? If this is wrong, can we roll back, or does it lock us in?

A single critical blocker means revise. All critical pass means implement. See Agent empathy → Scoring for why we use pass/fail/partial labels instead of numeric scores.

After a project ships, a closeout.md captures what actually happened vs. what was proposed. This is how the pipeline learns. Closeouts feed into future proposals as prior-art references.