Generative AI Agents for Controllable and Protected Content Creation

TL;DR

This paper introduces a multi-agent framework for generative AI that enhances controllability and content protection by integrating specialized roles and watermarking, ensuring aligned, trustworthy, and traceable content creation.

Contribution

It presents a novel multi-agent system combining controllable content synthesis with embedded watermarking for provenance protection during generation.

Findings

Achieves controllable and protected content creation in generative AI.

Integrates watermarking with content generation for ownership verification.

Formalizes a joint optimization for controllability, alignment, and robustness.

Abstract

The proliferation of generative AI has transformed creative workflows, yet current systems face critical challenges in controllability and content protection. We propose a novel multi-agent framework that addresses both limitations through specialized agent roles and integrated watermarking mechanisms. Unlike existing multi-agent systems focused solely on generation quality, our approach uniquely combines controllable content synthesis with provenance protection during the generation process itself. The framework orchestrates Director/Planner, Generator, Reviewer, Integration, and Protection agents with human-in-the-loop feedback to ensure alignment with user intent while embedding imperceptible digital watermarks. We formalize the pipeline as a joint optimization objective unifying controllability, semantic alignment, and protection robustness. This work contributes to responsible generative AI by positioning multi-agent architectures as a solution for trustworthy creative workflows with built-in ownership tracking and content traceability.