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 evidenceThe 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 architectureAEG 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 evidenceAcross 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 architectureSHIELD Lab #001In validation
SHIELDTemporal v2
Independent Evidence Reinforcement
Can correlation-aware evidence distinguish genuine corroboration from repeated, shared, or stale observations?
Current evidenceTemporal 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 architectureAEG × 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 evidencefeedback-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 architectureLab 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 hypothesisA falsifiable claim or explicit learning objective.
02Method and provenanceFixed inputs, versions, setup, artifacts, and reproduction commands.
03Measurements and behaviorObserved results, counterexamples, workflow behavior, and verification evidence.
04Limitations and revisionThreats to validity, boundaries, and what changed because of the evidence.
Read the complete lab standard