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Generative AI Agents for Controllable and Protected Content Creation

Haris Khan, Sadia Asif

TL;DR

The paper tackles controllability gaps and content provenance in generative AI by proposing a five-agent pipeline (Director/Planner, Generator, Reviewer, Integration, Protection) that embeds imperceptible watermarks during synthesis. It formalizes a joint optimization objective that unifies planning, semantic alignment, coherence, and protection, enabling end-to-end controllable and provenance-protected content creation with human-in-the-loop feedback. An integrated Protection agent, together with CLIP-based alignment and coherence optimization, is shown to enhance ownership tracking without compromising output quality, and an explicit experimental plan with metrics (e.g., $\text{CLIPScore}$, $\text{FID}$, watermark recovery) guides validation. The framework has practical significance for trustworthy creative workflows, enabling traceability, ownership, and governance-compliant generation in multimodal content creation.

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.

Generative AI Agents for Controllable and Protected Content Creation

TL;DR

The paper tackles controllability gaps and content provenance in generative AI by proposing a five-agent pipeline (Director/Planner, Generator, Reviewer, Integration, Protection) that embeds imperceptible watermarks during synthesis. It formalizes a joint optimization objective that unifies planning, semantic alignment, coherence, and protection, enabling end-to-end controllable and provenance-protected content creation with human-in-the-loop feedback. An integrated Protection agent, together with CLIP-based alignment and coherence optimization, is shown to enhance ownership tracking without compromising output quality, and an explicit experimental plan with metrics (e.g., , , watermark recovery) guides validation. The framework has practical significance for trustworthy creative workflows, enabling traceability, ownership, and governance-compliant generation in multimodal content creation.

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.
Paper Structure (37 sections, 10 equations, 2 figures, 1 table, 1 algorithm)

This paper contains 37 sections, 10 equations, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Multi-Agent Generative Systems
  4. Generative AI and Information Retrieval
  5. Protective AI and Content Provenance
  6. Proposed Multi-Agent Framework
  7. Architecture Overview
  8. Agent Roles and Responsibilities
  9. Interactive Control Loop
  10. Mathematical Formulation
  11. Notation
  12. Planner Agent – Subtask Decomposition
  13. Generator Agent – Component-wise Synthesis
  14. Reviewer Agent – Semantic Alignment Scoring
  15. Integration Agent – Coherence Optimization
  16. ...and 22 more sections

Figures (2)

  • Figure 1: Proposed multi-agent framework. The pipeline consists of specialized agents for planning, generation, review, integration, and protection, with iterative human-in-the-loop feedback and provenance logging to ensure both fidelity to user intent and secure content traceability.
  • Figure 2: Illustrative case study of the multi-agent pipeline using the prompt "Red dragon flying over a castle at sunset." The Planner decomposes subtasks, the Generator synthesizes components, the Reviewer refines alignment, the Integration agent harmonizes layout, and the Protection agent embeds an imperceptible watermark for secure provenance.