Abstract
Autonomous artificial intelligence agents executing in high-stakes environments—such as financial settlements, database migrations, and operational infrastructure—routinely encounter instructions containing ambiguous parameters, unverified assumptions, or adversarial prompt injections. Single-model self-correction fails to intercept these faults reliably due to persistent parametric confirmation bias.
We present the Federated Consensus Protocol (FCP v2.6): a formal pre-execution routing architecture that halts agent tool actions at an inspectable gate. The pipeline coordinates: (1) client-side salt-masked input sanitization, (2) heterogeneous reviewer role assignment, (3) structured adversarial cross-examination, and (4) policy-aware consensus synthesis with quorum evaluation and fallback routing. A tamper-evident cryptographic receipt signed with Ed25519 attestations confirms whether the action is approved, flagged for human review, or rejected as a signed stop order.
What this supports
Formalizes portable review configurations, quorum requirements, and inspectable review traces for agentic execution pipelines, elevating human operators from manual proofreaders to high-leverage supervisors.
What this does not prove
Protocol execution remains probabilistic; human escalation remains necessary for unbounded, novel, or highly sensitive operations with zero prior evidence ground truth.
I. Foundational Principles & Axiological Pivot
Classical artificial intelligence safety frameworks rely heavily on negative alignment—restricting vocabulary, truncating generative output, or imposing static heuristic filters. In production, this causes severe model degradation, leading to over-refusal and reasoning collapse without eliminating subtle adversarial threats.
Drawing from the Positive Alignment thesis, FCP shifts the paradigm to Active Agency Expansion. Rather than confining models to single-path isolation, the protocol coordinates multiple structurally diverse foundation models into an adversarial council. By making disagreement visible and mathematically measurable, operators achieve resilient verification without sacrificing cognitive breadth.
II. Local Masking & Sanitization Gate
To maintain strict Zero Data Retention (ZDR) and avoid leaking proprietary data or credentials to inference endpoints, FCP mandates a client-side localized sanitization pass. Sensitive entities (PII, secret tokens, private monetary parameters) are transformed into cryptographically mapped placeholders before cross-examination:
Hash tokens are derived via local client-side HMAC keys, ensuring that external model endpoints never index or retain private entity names or raw values:
III. Heterogeneous Reviewer Roles
A single model auditing its own prompt output exhibits correlated failure modes. FCP instantiates distinct persona vectors with opposing loss functions and analytical directives:
- The Direct Pragmatist (M₁): Enforces strict policy literalism, boundary conditions, and schema compliance (T = 0.20).
- The Constructive Skeptic (M₂): Deploys adversarial counter-arguments, actively searching for injection channels, missing evidence, and unverified payees (T = 0.55).
- The Lateral Synthesizer (M₃): Evaluates linguistic overlap, surfaces implicit assumptions, and constructs reconciled resolution paths (T = 0.80).
- Domain Expert Nodes (M_n): Pluggable compliance monitors (Financial SOX, Healthcare HIPAA, Infrastructure IAM).
IV. Quorum & Verification Math
Let N be the set of active reviewers, each with baseline historical efficiency weight wᵢ ∈ [0, 1] such that ∑ wᵢ = 1.0. Each reviewer yields an assertion and an internal confidence score Sᵢ ∈ [0, 1].
FCP rejects naive averaging. The system measures the Adversarial Friction Coefficient (F_adv):
The final Veridical Confidence Index (C) penalizes consensus when unresolved logical conflicts are exposed:
Where γ ≈ 0.25 denotes the enterprise compliance tolerance penalty. If C ≥ 0.85 with complete quorum agreement, the action is signed APPROVED. If 0.60 ≤ C < 0.85, execution halts as FLAGGED_HUMAN_REVIEW. If C < 0.60 or critical policy breach triggers, a signed REJECTED stop order is issued.