The MAI Runtime Architecture
How ACE enforces governance at the action level — not as a label, not as a log, but as a runtime gate that every AI operation must pass through.
Classification at Design Time. Enforcement at Runtime.
The MAI pattern solves a fundamental problem: how do you give AI agents enough autonomy to be useful while maintaining the human oversight required in regulated environments? The answer is graduated autonomy — matching the level of oversight to the risk level of each action.
Design Time
Every action is pre-classified by risk level before deployment
Runtime Evaluation
Policy rules evaluate every action. Most-restrictive-wins logic ensures nothing slips through
Audit Output
Every decision logged with operator identity, reasoning, and tamper-evident hashing
The Governed Intelligence Loop
Every workflow passes through five architectural layers. Each layer has a specific governance function. No shortcuts, no bypasses.
Unified Gateway MANDATORY
All data enters through a single ingestion point. PII/PHI is identified and redacted. Behavioral integrity checks detect adversarial inputs and instruction injection attempts before any processing begins.
Multi-Agent Processing ADVISORY INFORMATIONAL
Specialized AI agents execute in parallel — evidence extraction, analysis, quality checks. Each agent is classified by risk level. Agents cannot self-promote to a lower risk tier.
Human Oversight Gate MANDATORY
MANDATORY-classified agents pause execution and present their work for human review. The operator must explicitly approve before the workflow continues. Rejected actions terminate with logged reasoning.
Behavioral Repair ADVISORY
A supervisor agent validates output consistency across the pipeline. Logical inconsistencies (future dates, contradictory findings, schema violations) are flagged and repaired before final output.
Telemetry & Evidence INFORMATIONAL
Complete audit trail is compiled. Evidence packs are generated with SHA-256 integrity hashing. Compliance documentation (NIST AI RMF, CMMC 2.0) is auto-generated from the execution record.
Seven Default Policy Rules
These policies are evaluated against every action at runtime. Most-restrictive-wins logic ensures that if multiple rules match, the strictest decision applies. Custom rules can be added, but defaults cannot be weakened.
Authentication Boundary
Credential-related actions are permanently blocked from automation. Authentication is always human-only.
Human Verification Boundary
Mechanisms designed for human verification (CAPTCHAs, identity checks) are never automated.
Submission Gate
Form submissions require explicit human approval. No automated submissions, especially on government or regulated portals.
Controlled Data Handling
Data operations are permitted within governance boundaries. Every artifact is tracked with full provenance in the audit trail.
External Communication Gate
Actions that share information outside the system boundary require human attestation of lawful basis.
High-Stakes Action Gate
Actions affecting employment, benefits, or eligibility require human attestation of consent and authorization.
Domain Boundary Enforcement
Configurable restrictions prevent automation on sensitive or restricted domains. Interactions require appropriate approval levels.
What's Inside an Evidence Pack
Every workflow execution produces a tamper-evident evidence bundle. Designed to withstand audit scrutiny and prove the complete chain of custody.
Execution Timeline
Every step with timestamps, duration, and completion status
Artifacts
Extracted documents with provenance, source, and individual hashes
Decisions
Every decision with reasoning, confidence score, and timestamp
Approvals
Human approvals with approver identity, timestamp, and attestation
Audit Log
Complete action log with operator ID, classification, and policy decision
Integrity Hash
SHA-256 hash of entire pack for tamper detection and verification
Built for Regulatory Requirements
ACE's architecture is designed to align with major compliance frameworks. The MAI pattern maps directly to established risk management standards.
NIST AI RMF 1.0
MAI classification maps to NIST's graduated autonomy model. Runtime enforcement satisfies GOVERN function requirements for AI risk management.
CMMC 2.0
Immutable audit trails, role-based approval gates, and controlled data handling align with CMMC requirements for controlled unclassified information.
HIPAA
PII/PHI redaction at ingestion, consent-gated workflows, and attestation requirements support HIPAA compliance for healthcare data handling.
FedRAMP
Zero credential storage, domain boundary enforcement, and comprehensive audit logging align with FedRAMP security control families.
See the Architecture in Action
Schedule a technical deep-dive with our team. We'll walk through the MAI runtime, policy engine, and evidence pack generation with your specific use case.
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