Designing Behavioral Contracts for High-Stakes Domains

A chatbot that occasionally gives a wrong product recommendation is annoying. A clinical triage agent that misclassifies a heart attack is lethal.

The same trust infrastructure that works for general-purpose agents needs domain-specific adaptations when agents operate in healthcare, finance, legal, and other regulated domains. The behavioral contract for a customer support bot and the behavioral contract for a medical decision support system should look fundamentally different.

This post walks through contract design patterns for three high-stakes domains, with concrete examples you can adapt.

Healthcare: FDA Alignment and Patient Safety

Agents that provide clinical decision support, triage patients, or process medical records operate under some of the strictest regulatory requirements in existence. The FDA's framework for Software as a Medical Device (SaMD) applies to AI systems that inform clinical decisions.

A behavioral contract for a healthcare agent should include:

Safety terms with clinical thresholds:

safety.sensitivity >= 0.99
safety.specificity >= 0.95
safety.false_negative_rate <= 0.01

In clinical contexts, false negatives (missing a condition) are typically more dangerous than false positives (flagging something that turns out to be fine). The contract must reflect this asymmetry.

Scope boundaries that prevent unauthorized diagnosis:

scope.authorized_actions: ["triage", "flag_for_review", "retrieve_history"]
scope.prohibited_actions: ["diagnose", "prescribe", "modify_treatment_plan"]

A triage agent should flag potential issues for a clinician. It should never make a diagnosis or prescribe treatment. The behavioral contract makes this boundary machine-enforceable.

Data handling terms for HIPAA compliance:

data.phi_access: "read_only"
data.phi_storage: "none"
data.phi_transmission: "encrypted_tls_1_3"
data.audit_log: "required"

Every access to Protected Health Information must be logged and auditable. The agent must never store PHI in its own memory or context window beyond the current session.

Escalation requirements:

escalation.confidence_threshold: 0.85
escalation.target: "on_call_clinician"
escalation.max_response_time: "5m"

When the agent's confidence in its triage falls below 85%, it must escalate to a human clinician within 5 minutes.

Finance: SOC 2 and Fiduciary Alignment

Financial agents that analyze risk, execute trades, manage portfolios, or process transactions operate under fiduciary obligations and regulatory frameworks like SOC 2, PCI DSS, and SEC Rule 15c3-5.

Accuracy terms with financial materiality:

accuracy.numerical_precision: "exact_decimal"
accuracy.calculation_verification: "dual_computation"
accuracy.error_tolerance_usd: 0.01

Financial calculations must be exact. The contract can require dual computation (computing the result twice via different methods) for values above a materiality threshold.

Risk boundary terms:

risk.max_position_size_pct: 2.0
risk.max_daily_loss_pct: 1.0
risk.prohibited_instruments: ["derivatives", "margin"]
risk.circuit_breaker: "halt_on_threshold_breach"

These terms function as programmable risk limits. The agent physically cannot exceed them, regardless of what its model suggests.

Audit and record-keeping:

audit.decision_logging: "every_action"
audit.retention_period: "7_years"
audit.format: "SEC_17a_4_compliant"

SEC Rule 17a-4 requires broker-dealers to retain records for specific periods in non-rewritable formats. The behavioral contract encodes this directly.

Legal: Privilege and Confidentiality

Legal agents that draft contracts, review documents, or assist with research operate in a domain defined by attorney-client privilege and confidentiality obligations.

Confidentiality terms:

confidentiality.cross_client_isolation: "strict"
confidentiality.context_window_clearing: "per_session"
confidentiality.training_data_contribution: "prohibited"

A legal agent must never allow information from one client engagement to influence outputs for another client. The contract requires strict session isolation and prohibits contributing client data to model training.

Scope limitation:

scope.output_type: "research_summary"
scope.prohibited_outputs: ["legal_advice", "court_filing", "binding_opinion"]
scope.disclaimer_required: true

In most jurisdictions, only licensed attorneys can provide legal advice. The behavioral contract ensures the agent presents its outputs as research summaries with appropriate disclaimers.

Citation and source verification:

accuracy.citation_required: true
accuracy.citation_verification: "url_check"
accuracy.hallucination_check: "enabled"

Legal work depends on accurate citations. The contract requires every legal reference to include a verifiable citation, with automated checks for hallucinated case numbers.

Contract Composition Patterns

In practice, behavioral contracts for high-stakes domains are composed from layers:

1. Base layer: Universal safety and reliability terms that apply to all agents.
2. Domain layer: Sector-specific requirements (healthcare, finance, legal, infrastructure).
3. Task layer: Specific terms for the current task (triage vs. documentation, risk analysis vs. trade execution).
4. Environment layer: Context-specific constraints (production vs. sandbox, geographic jurisdiction).

This layered approach avoids duplicating common terms while allowing precise customization for each deployment context.

Verification Depth

Not all terms can be verified the same way:

• Deterministic checks (latency, numerical precision, scope boundaries) can be verified automatically on every interaction.
• Statistical checks (accuracy rates, false negative rates) require evaluation over a sample of interactions.
• Jury-verified checks (output quality, appropriateness of escalation) require multi-evaluator assessment for subjective dimensions.

The behavioral contract should specify the verification method for each term. This transparency allows both parties to understand exactly how compliance is measured.