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**Policy Memo: Managing Agentic AI Liability with the Phase Mirror Framework**
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### **I. PURPOSE: THE EMERGING RISK LANDSCAPE OF AGENTIC AI**

The adoption of agentic AI systems introduces a new class of liability
risks that differ fundamentally from predictive models. Unlike
predictive AI, where accountability rests with a human user, agentic
systems act autonomously, moving liability from the user to the system
designer and exposing the organization to novel risks. This memorandum
establishes the \"Phase Mirror\" framework as our required methodology
for governing these risks through explicit mechanisms, not abstract
principles.

Proactive governance is a fiduciary duty. The Securities and Exchange
Commission (SEC) is scrutinizing how companies disclose material AI
risks, making a defensible governance architecture non-negotiable. This
begins with a clear-eyed acknowledgement of the core tension between AI
autonomy and enterprise governance.

### **II. THE CORE DISSONANCE: AI AUTONOMY VS. ENTERPRISE GOVERNANCE**

Effective management of agentic AI requires a clear understanding of the
fundamental conflict at its heart. This is not a problem to be solved,
but a permanent tension that must be managed through explicit bindings.
Agentic AI promises autonomous decision-making and action; enterprise
governance demands deterministic guarantees, audit trails, and human
accountability.

These forces are in opposition. The Phase Mirror methodology confronts
this conflict by replacing vague \"vibe claims\" with concrete actions
and routing abstract complaints to specific mechanisms: **issuance,
access, incentives, and liability.** Instead of aspiring to \"align\"
AI, we will build systems that bind its behavior to our policies,
allowing us to identify the specific liability exposures this core
tension creates.

### **III. ANALYSIS OF KEY LIABILITY EXPOSURES**

Applying the Phase Mirror framework to agentic systems reveals
predictable dissonances. These are not system bugs; they are structural
conflicts that represent the primary sources of our legal and
operational liability.

-   **Probabilistic Outputs vs. Binary Compliance:** Agentic systems
    > produce probabilistic outputs based on Bayesian logic. Our legal
    > and compliance frameworks demand binary, yes/no adherence. This
    > friction is amplified in machine-to-machine decision chains,
    > complicating traditional liability models.

-   **System Autonomy vs. Deterministic Success:** An agent's capacity
    > for autonomous action implies a \"permission to fail\" as it
    > learns. This is in direct conflict with the enterprise's demand
    > for deterministic success in high-stakes environments, requiring
    > tiered governance models (human-in-the-loop vs. human-on-the-loop)
    > to manage unacceptable risk.

-   **\"Reasoning\" Capability vs. Data Integrity:** An agent\'s ability
    > to perform causal reasoning exposes previously hidden gaps in our
    > data hygiene. Faulty, biased, or incomplete data leads an agent to
    > generate wrongful conclusions, creating direct liability for its
    > actions.

-   **\"No-Code\" Interfaces vs. Causal Expertise:** The accessibility
    > of \"no-code\" tools masks the deep expertise required to build
    > valid causal models. This creates a significant risk of employees
    > with insufficient expertise building agents that produce faulty,
    > liability-creating conclusions.

-   **Transparency Rhetoric vs. Proprietary Systems:** Our reliance on
    > proprietary, third-party platforms can create \"black box\"
    > scenarios where an agent\'s decision logic is opaque. This
    > prevents accountability, makes audits impossible, and renders the
    > agent\'s actions indefensible.

These tensions are not independent; they converge on a single,
structural tradeoff that cannot be delegated to engineering. It is a
governance decision that leadership must make explicitly.

### **IV. THE CRITICAL POLICY DECISION: THE COMPLIANCE-ACCURACY TRADEOFF**

The choice between optimizing for the most compliant outcome or the most
accurate one is not a system failure; it is a structural decision
requiring explicit policy. The Phase Mirror methodology demands this
tradeoff be named plainly. This is not a technical configuration; it is
the defining policy choice that will dictate our liability architecture.

The Precision Question we must answer is: **Does the agent optimize for
the most accurate outcome or the most compliant one?**

This question cannot have an abstract answer. The answer must be encoded
as formal policy with explicit triggers defining when compliance
overrides accuracy. When an agent\'s \"reasoning\" conflicts with its
\"rules,\" its behavior must be predetermined. The system must not
freeze or hallucinate a compliant answer. It must escalate the conflict
to a pre-defined human exception handler, accompanied by a logged
rationale.

### **V. RECOMMENDED GOVERNANCE MECHANISMS & LEVERS**

To enforce this policy, the following governance levers will be
implemented immediately.

  **Owner**    **Proposed Lever**                                                                           **Key Metric**             **Horizon**
  ------------ -------------------------------------------------------------------------------------------- -------------------------- -------------
  Governance   Define a formal \"error budget\" to set acceptable failure rates for agentic decisions.      Allowable Failure Rate %   30 days
  Legal        Map probabilistic outputs to defined corporate liability tiers.                              Audit Pass Rate            60 days
  Ops          Evolve oversight from \"human-in-the-loop\" to \"human-on-exception\" to scale monitoring.   Intervention Ratio         45 days
  Eng          Implement and stress-test agent autonomy against hard-coded safety guardrails.               Violation Rate 0%          21 days
  Data         Quantify causal model validity score *before* any production deployment.                     Graph Accuracy Score       14 days

These levers are not suggestions; they become binding through the
following governance artifacts.

### **VI. IMPLEMENTATION: BINDING GOVERNANCE TO ARTIFACTS**

Effective governance moves from abstract alignment to concrete
artifacts. The Phase Mirror heuristic is our mandate: replace the verb
\"align\" with an artifact. Therefore, the following are non-negotiable
implementation requirements for any high-risk agentic AI system.

-   **Spec:** A formal document defining what constitutes an
    > \"acceptable\" error versus a policy violation.

-   **Contract:** Clear contracts establishing liability tiers between
    > our organization and any third-party platform providers.

-   **SLA:** Service Level Agreements that commit to specific audit pass
    > rates and explainability standards.

-   **Dataset:** A mandatory requirement for causal model validity
    > scoring *before* any production deployment.

-   **Kill-Switch:** Governance triggers and rollback mechanisms for any
    > agentic plan that depends on hope or operates in a high-stakes
    > environment.

### **VII. CONCLUSION: MANAGE TENSIONS, DON\'T SOLVE CONTRADICTIONS**

The contradictions between AI autonomy and enterprise governance are not
bugs to be fixed. They are permanent tensions to be explicitly managed.
Our goal is not to solve them, but to govern them with rigor.

This framework provides the necessary tools to govern this tension. Our
mandate is to use them, ensuring innovation is bound by discipline and
liability is managed by design, not by hope.
