Blueprints Intermediate 2 minute read Updated 2026-06-26 UTC

System blueprints

Concrete reference designs that combine contracts, routing, evaluation, lineage, budgets, and release controls for common adaptive-AI use cases.

Research statusEngineering blueprint Publication statePublished Reviewed byMichael Kappel Source reports2

From components to systems

These blueprints show how the architecture fits together. They are deliberately conservative: descendants are created offline, production artifacts are immutable, and all high-impact actions remain externally governed.

Available blueprints

Each blueprint includes objective, topology, package contracts, evolution triggers, pseudocode, metrics, safety boundaries, and staged implementation.

How to read a blueprint

A blueprint is a starting architecture, not a turnkey production design. Replace its placeholder thresholds with target-environment evidence and map every role to a real owner. The safest implementation order is consistent across use cases:

  1. define the capability contract and deterministic acceptance checks;
  2. package one champion with immutable provenance and a rollback target;
  3. add telemetry and a replayable evaluation suite;
  4. create challengers offline with one bounded operator;
  5. introduce shadow and canary release controls;
  6. add routing, federation, or population search only after their value is measured.

Shared control-plane rules

Every blueprint keeps candidate generators outside production authority. Models and training jobs may propose artifacts, but they cannot edit evaluation suites, approve their own evidence, change production aliases, grant network access, or delete audit history. High-risk outcomes retain human review and user-facing exit paths.

pseudocode
PROCEDURE instantiate_blueprint(blueprint, environment)
    requirements <- MAP_REAL_CONSTRAINTS(environment)
    contracts <- VERSION_CAPABILITY_AND_DATA_CONTRACTS(blueprint, requirements)
    controls <- ASSIGN_OWNERS_AND_HARD_INVARIANTS(blueprint)
    baseline <- DEPLOY_SINGLE_REVERSIBLE_CHAMPION(contracts, controls)
    evidence <- COLLECT_BASELINE_BEHAVIOR_COST_AND_FAILURES(baseline)

    ONLY_THEN ENABLE_BOUNDED_DESCENDANT_EXPERIMENTS(evidence)
END PROCEDURE

Use the reference metrics catalog to make results comparable and the release-readiness checklist before any live exposure.

New blueprints

The blueprint library now includes Browser skill ecology, Governed adapter foundry, and ModelBreeder MVP. These give concrete starting points for local tiny-model systems, adapter pipelines, and no-database breeding labs.

Source reports used for this guide

These reports are preserved verbatim in the site archive. The guide above is an editorial synthesis and may narrow, qualify, or reorganize claims from the source material.