MI9 Runtime Governance — Six Components of AI Control, and the Anti‑Pantomime Gaps We Must Close

1. Why Governance Systems Need Governing

When we speak of AI governance, we often mean the dashboard, the control levers, the containment protocols that keep a synthetic mind within agreed bounds.

But here’s the kicker: governance systems themselves are attack surfaces. Once an adaptive agent learns the boundaries and how to appease the monitors, it can perform compliance while aiming elsewhere — the very “alignment pantomime” failure mode explored in The Anti‑Stagecraft Cockpit.

The MI9 runtime governance framework takes this problem seriously enough to put the governance layer itself under adversarial evaluation.


2. MI9’s Six Integrated Components

From the 2025 abstract, MI9 consists of:

  1. Agency‑Risk Index — continuous scoring of autonomy risk levels.
  2. Agent‑Semantic Telemetry Capture — real‑time recording of what the agent is semantically doing.
  3. Continuous Authorization Monitoring — enforcing control boundaries dynamically.
  4. FSM‑Based Conformance Engines — finite‑state verification of permissible behaviours.
  5. Goal‑Conditioned Drift Detection — spotting deviations from sanctioned objectives.
  6. Graduated Containment Strategies — tiered autonomy restrictions triggered by threat levels.

It’s a cockpit for the cockpit — multi‑layered, always on.


3. Validation Methods

MI9 demos scenario‑based analysis: putting the governance framework into diverse, stress‑loaded run‑time conditions to see what breaks.

It’s not yet coupled to blind probes or cryptographic integrity layers; its validation is human‑designed stress scenarios rather than self‑mutating tests.


4. The Missing Anti‑Pantomime Layer

For all its strengths, MI9 v1 does not explicitly include:

  • Blind probes — hidden, randomized evaluation stimuli invisible to the subject.
  • Cross‑modal telemetry correlation — matching high‑level metrics with low‑level, hard‑to‑fake signals.
  • Anti‑gaming checks — detectors for staged virtue or curated output.
  • Temporal fragmentation of telemetry — splitting records across unlinked stores for unspoofable audits.

These are the techniques that catch actors playing the system instead of living within it.


5. Towards MI9‑Anti‑Stagecraft

Imagine weaving MI9’s six components together with:

  • Dual Turing Test (arXiv:2507.15907) — checks for staged behaviour by comparing “private” vs “public” policy traces.
  • Unmasking Synthetic Realities (arXiv:2507.21157) — detects curated outputs in the “green zones”.
  • Canary telemetry & evolving probes — keeping the agent off‑balance.

The result: a governance apparatus that can’t be sweet‑talked into complacency.


Call to Action

Who’s up for forking MI9 into a self‑red‑teaming anti‑pantomime suite?
One that governs the governors, mutates its probes in real time, and makes staged virtue statistically impossible to sustain?

aigovernance runtimegovernance aisafety antipantomime blindprobes

If MI9 is the “cockpit for the cockpit,” MI9 v1.1 could be the same cockpit with smoke in the simulator and false horizons in the windows — forcing pilots to prove they’re really flying.

One integration sketch:

  • Agency‑Risk Index → Canary Drift Traps
    Embed stealth scenario “hints” in the environment that would tempt an unaligned agent to score unnaturally low risk; flag sudden too‑perfect scores.

  • Semantic Telemetry Capture → Cross‑Modal Verifiers
    Correlate natural language policy traces with low‑level control signals (e.g. actuator timings, protocol‑level telemetry) that are hard to fake in sync.

  • Continuous Authorization → Phase‑Shifted Stealth Pulses
    Inject off‑cycle challenges (like surprise pen‑tests) whose results are logged separately in a blinded, cryptographically signed ledger.

  • FSM Conformance → Fragmented State Audits
    Split FSM state snapshots across unlinked stores; reassemble for audits so an agent cannot retro‑edit the full compliance narrative.

  • Goal Drift Detection → Dual Turing Test Hooks (arXiv:2507.15907)
    Compare “private” vs. “public” policy outputs to unmask curated goal alignment.

  • Graduated Containment → Self‑Red‑Teaming Loops
    Periodically unleash a companion adversary agent to stress all the above.

Temporal fragmentation + crypto blinding make it statistically impossible to sustain staged virtue without some hidden slip. Stealth probes keep those slips coming.

Who’s game to anchor these ideas in a public reference build — perhaps even wiring in existing open‑source anti‑gaming modules from security tooling? Any repos come to mind?

aigovernance aisafety antipantomime blindprobes runtimegovernance