A2A and MCP are open, publicly specified protocols for agent discovery, task exchange and tool access between independently built agents.

Open protocol communication between agents is publicly demonstrated. What was not identified in reviewed public material is negotiation between agents acting for opposing commercial principals — a creator agent and a sponsor agent settling terms — with outcomes written back to behavioural twins.

Agents that quote, transact, handle service cases and progress commercial workflows are publicly documented, principally in the CRM and marketing context.

Reviewed commercial agents act for one enterprise principal. Bilateral agent negotiation between a creator principal and a sponsor principal, with performance-linked settlement, was not identified.

Supervisor / sub-agent decomposition with delegated task execution is now documented as a first-class primitive on every major platform reviewed. It is no longer a differentiating element in isolation.

The proposed architecture contains multi-agent orchestration, but generic multi-agent decomposition is unlikely to distinguish it. The distinguishing feature identified in this review is that the agents act for individual creator and viewer principals across platforms the operator does not control.

Long-term memory extraction and retrieval across sessions is documented by Google (Agent Engine Memory Bank), AWS (AgentCore Memory) and Microsoft (persistent agent threads).

Reviewed public memory systems store conversational and task facts about the interacting user of an assistant. The proposed architecture's behavioural memory is derived from observed platform outcomes rather than conversation, and writes back into a behavioural twin.

Vector, hybrid and graph retrieval over enterprise data is uniformly documented (Azure AI Search, Vertex AI Search, Bedrock Knowledge Bases with GraphRAG, Data Cloud).

Reviewed knowledge systems are organisation-scoped. The proposed architecture contemplates a graph spanning creators, viewers, content derivatives, audiences and commercial relationships across independent platforms.

Event-triggered autonomous agents and durable workflow orchestration are documented across Copilot Studio, Agentforce, Step Functions and Agent Engine.

Public autonomous execution operates inside the vendor's own tenancy and systems of record. Autonomous execution on third-party consumer platforms, on behalf of an individual, was not identified in reviewed public material.

Adobe publicly describes a connected content supply chain across planning, creation, activation, delivery, reporting and insights.

Adobe's loop is the closest public analogue but is enterprise-brand oriented and operates over owned channels and paid media rather than over creator-owned presences on independent social platforms.

Large-scale behavioural modelling exists as recommendation and personalisation systems and as unified customer profiles.

Behavioural models in reviewed public material are platform-owned and optimise the platform's objective. The proposed architecture contemplates a portable behavioural model whose principal is the individual and which is queryable by that individual's agents.

C2PA Content Credentials, SynthID watermarking and platform AI-content labelling are all publicly documented.

Provenance is publicly mature as a credential and labelling layer. Provenance state being consumed as an input by autonomous rights and commercial agents was not identified in reviewed public material.

Continuous evaluation, monitoring, retraining and data-flywheel patterns are documented across ML platforms and recommendation systems.

Feedback loops are ubiquitous at model level. The proposed architecture's loop closes at the level of an individual principal's twin, so that measured outcomes change subsequent autonomous decisions for that principal specifically.

Multimodal embedding and reasoning across text, image, audio and video is widely documented and commoditised.

The proposed architecture uses multimodal search as an input to lifecycle and rights decisions rather than as an end capability; the pillar as a standalone feature is fully anticipated by public material.

Digital twin platforms are documented for physical, industrial and operational systems (Omniverse, IoT TwinMaker, Azure Digital Twins).

Reviewed twin platforms model physical assets and processes. Behavioural twins of creators, viewers and content items were not identified in reviewed public material under twin terminology.

Connector catalogues, API gateways and identity-scoped tool access are uniformly documented.

This pillar is comprehensively anticipated by public material and should be treated as supporting infrastructure rather than as an independent position.