Solutions · EU AI Act Article 14 · Autonomous Agents
OMEGA for AI Oversight
Article 14 of the EU AI Act requires that high-risk AI systems be designed for meaningful human oversight. The Act does not say what evidence of meaningful oversight looks like. OMEGA does.
The Article 14 gap
Article 14 of Regulation (EU) 2024/1689 (the AI Act) obliges providers and deployers of high-risk AI to design and operate systems "in such a way that they can be effectively overseen by natural persons during the period in which they are in use." Effective oversight, the Article states, means a natural person can intervene, override, or stop the system, and can interpret its output correctly.
Article 12 separately requires automatic recording of events. But Article 12 logs answer "what did the system do?" - they do not answer "did meaningful oversight occur, by whom, with what evidence, and against what expected outcome?" That is the question a market surveillance authority will ask. A log cannot answer it.
What an OMEGA AI-oversight record contains
For each consequential action of a high-risk AI system, an OMEGA record binds:
- Authority - the named human overseer and their authorisation scope (
P1). - Freshness - a temporal attestation that the human was present and authenticated within a declared window before the action (
P1_Freshness). Without this, a session that authorised an action hours earlier produces records the human never approved. - Reasoning - a directed-acyclic graph of evidence, each node grounded in fact, declared unknown, or named assumption (
P2,P2_DAG). - Rejected alternatives - the options considered and the basis for their rejection (
P2). - Expected outcome - the trajectory the action commits to, and the environmental invariants under which that commitment holds (
P4,P4T_EnvInvariant). - Atomic agency - sub-processes cannot bypass governance by claiming "internal function" status (
P6_AtomicAgency). - Chain integrity - a SHA-256 hash chain that detects tampering with any past record (
P_ChainIntegrity).
Worked example: agentic action
Scenario. An autonomous customer-service agent is permitted to issue refunds up to £500 without human review. It encounters a request for £750. Under Article 14 a natural person must be able to oversee and intervene.
Conventional log. The trace records: request received, policy engine returned "requires manual review," ticket opened, agent A. Smith approved at 14:32, refund issued. The market surveillance authority asks: did A. Smith actually see the alternatives the agent considered? Were they authenticated at 14:32 or was that a stale session? What was the agent's expected outcome of the refund? The log cannot answer.
OMEGA record. P1 binds the named authority (A. Smith, refunds-officer role) with scope ≤ £1,000. P1_Freshness records a re-authentication completed at 14:31:48, valid for a 5-minute window. P2 records the reasoning DAG with two alternatives considered (refund denial, partial refund) and the basis for rejecting each. P4 records the expected outcome (customer retained, NPS recovery), with P4T_EnvInvariant noting the assumption that the customer's billing history is not flagged. The record is hash-chained.
Deployment shapes
An OMEGA AI-oversight record can be produced in three positions relative to the high-risk system:
- Sidecar. The runtime sits beside the AI system and emits a record per consequential action without modifying the model code. Best when the AI system is treated as a black box or a vendor-supplied component.
- Embedded SDK. Records are produced in-process at the decision point. Best for agent frameworks built in-house, where the reasoning graph is already available.
- Post-hoc reconstruction. Existing logs and traces are mapped into the OMEGA schema retrospectively. Useful for incident review, but the freshness conjunct (
P1_Freshness) cannot be reconstructed after the fact and must be declared as missing.
What this does not do
OMEGA does not make the AI system safe. It does not constrain model behaviour, replace red-teaming, or substitute for the substantive risk-management obligations of Articles 9 and 15 of the AI Act. It produces the artifact a market surveillance authority can read when asking whether meaningful human oversight occurred - against a fixed schema rather than an open-ended narrative.
See the AI Oversight example record · Why a log is not a governed record · Read the specification · Run the proof · All solutions