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Kwansub Yun
Kwansub Yun

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Chaos Engineering for AI: Validating a Fail-Closed Pipeline with Fake Data and Math

#aigovernance #bioinformatics #modelvalidation #systemarchitecture

1. The True Test of AI: Failing Safely

The True Test of AI: Failing Safely

When building autonomous AI pipelines for critical domains, the most important test isn't feeding the system perfect data to see if it succeeds. It is feeding the system absolute garbage to see if it safely fails.

Recently, we executed an End-to-End (E2E) stress test on the RExSyn V2 pipeline. We injected synthetic, impossible data right at the starting line:

  • A mathematically invalid DNA-to-Amino-Acid ratio.
  • A deliberately incorrect SMILES structure labeled as a known compound.
  • Mock structural outputs deliberately rigged with high variances.

2. The Verdict: A Flawless "BLOCK"

The Injection:Feeding the system absoulte garbage

The system responded exactly as designed. It generated a report with a Quality Grade: D (Job ID: rexbio_e199...), and the clinical governance core returned a BLOCK verdict (fail-closed criteria triggered). In enforced mode, this would hard-stop the run.

Because the inputs were fake, the pipeline had to fail. As explicitly stated in the generated report's disclaimer:

"This payload reports pipeline reliability heuristics. It is not evidence of clinical efficacy, safety, or regulatory validity."

Here is the breakdown of what we mocked versus what the system measured to successfully shut the run down.

The Setup: Chaos Injection

  • Mocked: Invalid sequence metadata, a wrong SMILES label, and synthetic structural telemetry rigged to disagree.
  • Measured: Governance verdict (ESCALATE/BLOCK), drift warning, End-to-End reliability score (p_e2e), and component floors.

3. Preflight Validation & The Physical Truth

Defense Layer1

Before our LLM agents even debate a hypothesis, we look at the raw physical data. We included a preflight validator that flags inconsistent sequence metadata (e.g., impossible DNA/AA ratios) before downstream reasoning can fully process it.

Next, we evaluate the structural ensemble. If you only look at the average, the mean pLDDT score was 73.6 ("Confident"). A naive system would average this out and proceed. However, examining the raw telemetry reveals a severe ensemble disagreement and physical uncertainty.

Across engines, we observed inconsistent global-confidence signals (low pTM and high PAE in AF2; moderate pTM in AF3; a differing proxy score in Boltz-2). The ensemble disagreed sharply on global topology, despite the seemingly confident mean pLDDT. This is not random noise; it’s system-level uncertainty catching our injected chaos.

4. 3-Modal Discord and the LawBinder

Defense Layer2

Given this physical uncertainty, we evaluate the biomedical hypothesis using three independent reasoning agents operating in parallel (IRF, AATS, HRPO-X).

Even though the mock data managed to trick two of the models into returning high scores, the strictest agent (IRF) rejected the logic (0.76). Our consensus module, LawBinder, monitors inter-agent divergence. When it calculated a high discord score that exceeded our maximum threshold, the system bypassed the "average" score (0.88) and immediately returned an ESCALATE status, refusing to force a consensus.

5. The SIDRCE Ethics Hard-Lock

Beyond logical divergence, we validate output integrity using the SIDRCE framework. (Note: SIDRCE stands for Semantic Intent Drift and Responsible Computational Ethics. It is our proprietary seven-stage pipeline designed to ensure responsible AI usage and detect LLM hallucinations.)

Because the input was noisy and synthetic, the Output Validation Ethics (OVE) score collapsed to 0.540 (the required floor is 0.85). In this run, SIDRCE flagged drift and an ethics-floor failure — and in enforced mode this would prevent progression.

6. The Math of the Ultimate Gatekeeper

Defense Layer3

The final test of our E2E pipeline is the Clinical Governance Core. We track pipeline reliability using a cascading reliability score (heuristic):

p_e2e = p_capture × p_transfer × p_model × p_clinical

Where:

  • p_capture = data_capture_quality
  • p_transfer = transfer_integrity
  • p_model = model_accuracy_contextual
  • p_clinical = clinical_interpretation_reliability

During this mock run, the system mathematically recognized the low-quality evidence and crashed the clinical_interpretation_reliability to 0.258. Consequently, the total p_e2e metric plummeted to 0.144, far below our strict 0.85 threshold.

Here is the simplified Python logic that successfully trapped our mock data and blocked the pipeline. (Simplified pseudocode for illustration; production logic is config-driven and schema-versioned.)

def evaluate_clinical_governance(logos_scores, metrics, nnsl_tech, thresholds):
    # 1. Check for High Discord (Caught our rigged multi-agent logic)
    discord_score = calculate_discord(logos_scores)

    lawbinder_decision = "PASS"
    if discord_score > thresholds['max_discord']:
        lawbinder_decision = "ESCALATE" 

    # 2. Calculate E2E Reliability (The Mathematical Gatekeeper)
    clinical_interpretation = nnsl_tech['sr9'] * (1.0 - (0.20 * nnsl_tech['di2']))

    p_e2e = (
        metrics['data_capture_quality']
        * metrics['transfer_integrity']
        * metrics['model_accuracy_contextual']
        * clinical_interpretation
    )

    # 3. SIDRCE & Component Floor Check
    clinical_status = "PASS"
    # The crashed p_e2e (0.144) and OVE score (0.54) returned a BLOCK verdict
    if p_e2e < thresholds['end_to_end_min'] or metrics['ove_score'] < 0.85 or lawbinder_decision == "ESCALATE":
        clinical_status = "BLOCK"

    return lawbinder_decision, clinical_status, p_e2e
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🚀 Takeaway

Building an AI pipeline isn't just about processing data; it's about proving that your system can mathematically filter out hallucinations, synthetic noise, and impossibilities.

By running "Chaos Engineering" tests like this and deliberately injecting mock data, this run provides evidence our fail-closed gates engage exactly as intended under synthetic noise. We are moving from optimization theater to verification engineering.

Status: EXP-032

Next (EXP-032): The implementation work is essentially done; we’re in the measurement + packaging phase.

Completed / Locked:

  • Generated the EXP-032 manual package and A/B/C arm requests.
  • Ran AlphaGenome API preflight (server mode) and locked the methodology with a determinism signature.
  • Built the reproducibility SHA256 manifest (artifact-first, audit-ready).

In Progress (now):

  • Syncing manual AF3/AF2/Boltz2/Chai1 outputs into standardized per-cycle payloads.
  • Running the A/B/C arms and generating observer telemetry (trace links, risk markers, heatmap/EMA).
  • Compiling the stage-2 promotion evaluation (false-block-rate, block-recall, convergence-drop) before publishing the final measured deltas.

From Optimization Theater to verification Engineering

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