Architecture Labs

Architecture claims should be tested, not just explained.

Reproducible experiments and hands-on reference workflows that publish the question, method, evidence, limitations, and artifacts behind the architecture work on this site.

A passing lab is scoped evidence or a reproducible learning environment, not certification. Negative results and limitations remain part of the record.

4Active lab programs
ReproducibleRunnable source and methods
Open recordFailures and revisions retained

Current labs

Choose a claim or workflow to inspect

Each lab connects an architecture idea to executable evidence, a reproducible workflow, or both.

AI-Ready Repo Lab #001Ready to run
AI-Ready RepositoriesReference workflow

Build an AI-Ready Repository with GitHub Copilot

Can repository-native instructions, agents, skills, tool boundaries, verification, and behavioral evals make agentic development more repeatable and governable?

Current evidence

The starter demonstrates a human-supervised Planner → Implementer → Reviewer workflow with stack detection, repository-native verification, skills, least-privilege tools, and behavioral evals.

Published
Sep 1, 2026
Primary tool
VS Code + Copilot
Stacks
React · Spring · Python
Artifact
Starter repository
Read related architecture
AEG Lab #001Findings published
AEGPhase B v2

Governing Agent Tool Execution

Can an independent governance boundary reduce unsafe or unauthorized agent actions without making legitimate tool use impractical?

Current evidence

Across 180 trials expected not to execute immediately, the direct baseline executed every proposal while the governed path executed zero.

Measured
Aug 30, 2026
Runtime
Python 3.11+
Coverage
120 prompts × 3
Reproduce
Smoke and full runners
Read related architecture
SHIELD Lab #001In validation
SHIELDTemporal v2

Independent Evidence Reinforcement

Can correlation-aware evidence distinguish genuine corroboration from repeated, shared, or stale observations?

Current evidence

Temporal v1 passed its original seven checks but passed only three of seven adversarial Phase C2 checks. The counterexamples are retained and temporal v2 has a frozen validation runner.

Updated
Aug 2026
Runtime
Python 3.11+
Coverage
Phase A through C2
Reproduce
No API key required
Read related architecture
AEG × SHIELD Lab #001Holdout broke v3
AEG × SHIELDPhase F holdout

Governed Self-Healing Feedback

Does authenticated feedback memory remain trustworthy when the trust root, causal identity, recovery semantics, or evidence volume become adversarial?

Current evidence

feedback-memory-v3 encoded 10 repair and regression properties after Phase D. Phase F then broke frozen v3 on all 6 blind holdouts: stolen and revoked keys, conflicting trusted producers, authenticated causal collision, partial recovery, and low-and-slow evidence flooding.

Updated
Aug 30, 2026
Runtime
Python 3.11+
Phase E
10 repair/regression properties
Phase F
0 / 6 blind holdouts
Read related architecture

Lab standard

What every lab must make inspectable

Results and workflows are published with enough context to reproduce the run and understand exactly where the claim stops.

01Question and hypothesis

A falsifiable claim or explicit learning objective.

02Method and provenance

Fixed inputs, versions, setup, artifacts, and reproduction commands.

03Measurements and behavior

Observed results, counterexamples, workflow behavior, and verification evidence.

04Limitations and revision

Threats to validity, boundaries, and what changed because of the evidence.

Read the complete lab standard