Metadata-Only Review Path

Test SMERC on your own action scenarios before a pilot

The runtime customer evaluation path lets a security, platform, or AI governance team run 5 to 25 metadata-only actions through SMERC. It produces posture decisions, recoverability scores, execution routes, Decision Lifecycle Ledger records, autonomy-budget impact, and a pilot-fit recommendation without requiring production access.

Evaluation Flow

What a reviewer actually does

The goal is to decide whether SMERC is worth a bounded shadow-mode pilot, not to claim production readiness from a synthetic test.

01

Prepare Actions

Copy the company metadata template and replace it with 5 to 25 proposed automated actions from one workflow.

Open company kit
02

Choose The Right Pack

Use general automation, cloud-admin, or Financial Runtime/SMERC-F depending on the workflow being reviewed.

Open workflow
03

Review The Report

Inspect posture counts, failed trust gates, highest-exposure actions, routes, controls, ledger validity, and autonomy state.

Open report
04

Decide Pilot Fit

If the output changes reviewer judgment, move to one workflow in observe mode with weekly labels.

Open pilot path

What SMERC Scores

The evaluation is about recoverability before execution

SMERC evaluates whether an automated action has enough evidence, containment, rollback capacity, and authority clarity to proceed. The output is not a legal approval or compliance certification. It is a replayable runtime posture that reviewers can compare against their existing process.

Irreversible exposureHow much harm remains if the action is wrong, late to cancel, broadly scoped, or difficult to roll back.
Reversible capacityHow much recovery path exists through rollback, containment, scoped execution, cancel reliability, and evidence quality.
Risk-adjusted authorizationWhether the action has enough recoverability and confidence to support a posture above block or freeze.
Autonomy budgetWhether the action stream is consuming too much risk, scope, or blocked/held attempts to continue independently.

Three Entry Points

A company reviewer should not need to decode the whole repo.

Pick the workflow family first, run the matching example second, and only consider a pilot if the report changes reviewer judgment.

General AI-agent and automation actions

Start with `examples/customer_eval_actions.json` for ordinary agent, deployment, tool-call, and high-impact workflow review.

Cloud Infrastructure and platform actions

Start with `examples/cloud_admin_customer_eval_actions.json` for IAM, network, Kubernetes, DNS, database, and backup actions.

Financial Financial Runtime / SMERC-F

Start with `examples/smerc_f_customer_eval_actions.json` for payment, refund, treasury, stablecoin, tokenized-collateral, wallet-policy, transaction-limit, and reserve-status actions.

Lifecycle Complete proof

Run `python -m reference_engine.complete_lifecycle_proof` to see the assembled loop: admission, pause, unlock, permit, execution result, and ledger.

Good Candidate Actions

  • AI coding agent proposes a production deployment.
  • MCP tool call can modify or delete customer data.
  • Cloud automation expands IAM permission scope.
  • Security automation quarantines systems or revokes access.
  • Finance workflow moves funds, issues refunds, or changes payment state.

Bad Candidate Inputs

  • Raw customer data or regulated records.
  • Production logs containing sensitive data.
  • Secrets, credentials, tokens, wallet keys, or private keys.
  • Full source-code dumps.
  • Anything the reviewer would not place in a normal security-review packet.

GitHub Actions Usage

Copy the workflow into a review repository and run it manually against a metadata-only JSON file.

name: SMERC Runtime Customer Evaluation

on:
  workflow_dispatch:
    inputs:
      action_file:
        required: true
        default: examples/customer_eval_actions.json

permissions:
  contents: read

jobs:
  evaluate-customer-actions:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v5
      - uses: actions/setup-python@v6
        with:
          python-version: "3.12"
      - run: python -m reference_engine.customer_evaluation "${{ inputs.action_file }}" \
          --json-output smerc-customer-evaluation/customer_evaluation_report.json \
          --markdown-output smerc-customer-evaluation/Customer_Evaluation_Report.md
      - uses: actions/upload-artifact@v4
        with:
          name: smerc-customer-evaluation-report
          path: smerc-customer-evaluation

What A Positive Result Looks Like

A positive evaluation does not mean SMERC is production-ready. It means reviewers found that recoverability scoring revealed meaningful differences from existing allow/deny review, identified useful constraints, or created a clearer escalation path for high-impact automated actions.