Bo
aae1a3af9a
Full-catalog skills audit + consolidation via ao skills retire: acfs cut; autodev→evolve, skill-auditor→heal-skill, beads-workflow→beads-br, continuity-loop→using-atm, inject→operationalize, forge→curate. Trigger surfaces preserved via absorbed-from notes; ao CLI surfaces unchanged; dispositions ledger flipped to historical rows; registries/codex twins/eval canaries regenerated and retargeted. Includes self-review fixes: ledger-aware DEAD_XREF in heal.sh, scenario-linkage allowlist retarget, codex-twin dead-path repairs, strengthened eval canaries, fold-survival guard for operationalize, and beads consolidation test hardening.
Operationalized: Google SRE AI Reliability Method
Google's 2026 SRE whitepaper "AI Engineering Reliable Operations" turned from a doc into executable, auditable artifacts — a kernel, an operator library, and validators — via the
operationalizing-expertiseskill (Track A: static distillation). This is the bridge that makes "are we actually doing what Google says?" a checkable artifact instead of a claim. Produced under beadag-4hf7; companion to the convergence section.
What's here
corpus/
primary_sources/google-sre-ai-reliable-operations.md # the paper (verbatim archive)
quote_bank/quote_bank.md # §1..§28 anchored quotes, fixed tag taxonomy
distillations/opus/distillation.md # single-model raw extraction
specs/triangulated_kernel.md # marker-bounded: 17 axioms + 14 operators (+ DISPUTED / UNIQUE)
specs/operator_library.md # 14 operator cards, each with an AgentOps-Enforcement verdict
scripts/
validate-corpus.py # anchors sequential, entries well-formed, tags in taxonomy
validate-operators.py # every operator has required fields + valid verdict + real anchors
extract-kernel.py # markers present, versioned, min axiom/operator counts
The scorecard (from the 2026-05-29 audit)
Each operator card records whether AgentOps actually enforces the behavior, where, and the gap bead. As of this writing: 7 ENFORCED · 6 PARTIAL · 1 GAP of 14 operators.
| Verdict | Operators |
|---|---|
| ENFORCED | Spec-Before-Code · Knowledge-To-Constraint · Reasoning-Execution-Decouple · Append-Only-Provenance · Pulled-Grounded-Context · Machine-Speed-Validation |
| SPLIT | Independent-Harness — ENFORCED at the skills layer (council/AP#7), PARTIAL at the CLI (no per-agent runtime guard → ag-w3fg) |
| PARTIAL | Progressive-Authorization (L0–L4 ladder → ag-wrom) · Tiered-Eval-Data (ag-fjbu) · Judge-Plus-Deterministic-Scoring (ag-xert) · Dry-Run-Before-Mutation (ag-r6l3) · Bounded-Interruptible-Loop (session-scope → ag-o5xp) · Non-Ambient-Identity (scoped) |
| GAP | In-Workflow-Golden-Capture (ag-xert) |
| DISPUTED | Fix-Forward vs. clean rollback (ag-7278) |
Headline: the one invariant AgentOps enforces that the convergence cluster consistently misses is Knowledge-To-Constraint — learnings compiled into gates, not prose. That's the sharpest edge.
Re-run the validators
cd .agents/operationalized/google-sre-reliability
python3 scripts/validate-corpus.py # OK corpus validation
python3 scripts/validate-operators.py # OK operator validation (14 operators)
python3 scripts/extract-kernel.py >/dev/null # OK kernel v1.0: 17 axioms, 14 operators
How to keep it honest
- When a gap bead closes, flip that operator's
AgentOps-Enforcementline to ENFORCED and update the scorecard here + inoperator_library.md. - When a new convergence signal lands in
docs/convergence/ledger.md, check whether it adds or sharpens an operator. - The kernel is versioned (
v1.0); bump it if axioms/operators change, and keep START/END markers intact (the extractor enforces this).