CreAgentive: Agent-Based Creative AI

• CreAgentive is a family of agent-based AI systems that combine goal-driven reasoning, autonomous planning, persistent memory, and adaptive learning for creative problem solving.
• Key implementations include multi-agent narrative generation, iterative visual art creation, and design workflows that harness dynamic human-AI collaboration.
• Empirical studies show robust performance with high quality-length scores, superior visual diversity metrics, and consistent output across extensive creativities.

CreAgentive defines a family of agentic computational systems structured for genuine creativity, integrating goal-driven reasoning, autonomous planning, memory, and adaptive learning processes. The term is used both generically—to characterize creative, agent-based AI as distinguished from prompt-driven generative AI—and as a technical referent for specific multi-agent platforms across textual, visual, interactive, and design domains. Across instantiations, CreAgentive systems exhibit structural features departing significantly from static, autoregressive content generators, emphasizing persistent internal state, agentic collaboration, mixed-initiative workflows, and robust mechanisms for creative synthesis and reflection.

1. Core Architectural Principles of CreAgentive Systems

CreAgentive platforms build upon multi-agent architectures encapsulating five distinguishing elements:

• Persistent Internal State: Each agent maintains a history-dependent latent vector (e.g., $S_t^{(i)}$ in the GAI framework), evolving over time through cycles of idea generation, scoring, introspection, and peer exchange. Agent states typically encode extracted concepts, positions on analogical mappings, and dialogue history (Sato, 25 Dec 2024).
• Heterogeneous Intrinsic Motivations: Agents are parameterized by weights $(\alpha_i, \beta_i, \gamma_i)$ attached to creativity-relevant scoring metrics such as Novelty, Importance, and Consensus. This diversity enables a spectrum of behaviors from exploration to convergence, promoting cross-fertilization (Sato, 25 Dec 2024).
• Hierarchical or Modular Team Topologies: Organizational layouts extend beyond simple round-robin dialogue (as in basic GAI), anticipating future expansions to hierarchical or graph-based agent teams. This allows for controlled information flow mimicking R&D group structure (Sato, 25 Dec 2024).
• Dialogue and Analogy-Driven Workflows: Agents follow structured protocols, e.g., five-phase Design-by-Analogy (DbA) cycles, where each phase targets a distinct epistemic action (functional mapping, mechanistic differentiation, solution transfer, challenge anticipation, opportunity identification) (Sato, 25 Dec 2024).
• Reflection, Critique, and Blending: Advanced instantiations implement explicit modules for memory retrieval, context-driven reasoning (e.g., reflective matching and blending via graph neural networks (Catarau-Cotutiu et al., 2022)), multi-agent critique/refinement (Venkatesh et al., 7 Apr 2025), and ongoing adaptation of goals or narratives based on intermediate evaluation.

2. Representative Implementations and Systemic Variants

2.1 Textual Narrative and Story Generation

The CreAgentive engine in (Cheng et al., 30 Sep 2025) and (Cheng et al., 5 Jul 2025) operationalizes the above principles in long-form narrative generation:

• Story Prototype: Abstract plot and character logic is maintained as a knowledge graph of semantic triples, decoupling narrative logic from language realization. This allows for sophisticated manipulations such as genre transfer, advanced literary device insertion (retrospection, foreshadowing), and robust cross-genre adaptation.
• Agent Workflow: Initialization agents construct a narrative skeleton; generation-stage agents instantiate chapter-level plots via candidate proposals scored for coherence, drama, and goal alignment; writing-stage agents synthesize prose, enforce advanced structures, and leverage state-tracking for retrospection/foreshadowing.
• Scalability and Evaluation: Empirically, CreAgentive demonstrates the capacity to generate thousands of chapters with consistent quality, achieving mean QLS (Quality-Length Score) of 4.78—close to human-authored references (4.96)—and per-chapter variance $\sigma \approx 0.05$ for over 2,700 consecutive chapters (Cheng et al., 30 Sep 2025).

2.2 Visual, Artistic, and Multimodal Creativity

The CREA framework (Venkatesh et al., 7 Apr 2025) positions CreAgentive methods as multi-agent pipelines for creative image generation/editing:

• Multi-Agent Roles: Specialized agents orchestrate creative direction, prompt decomposition (contrasting six creativity principles), generative execution (invoking off-the-shelf diffusion backbones), iterative critique (using multimodal LLMs), and adaptive self-enhancement via prompt refinement.
• Task Loop: Agents iteratively ideate, generate, critique, and refine visual content until a target Creativity Index is achieved. This agentic loop induces superior diversity, semantic alignment, and transformative edit quality compared to monolithic or one-shot baselines.
• Quantitative Gains: CREA outperforms reference models on diversity (LPIPS $0.709$), normalized user-rated novelty/utility, and aggregate LLM-Judge scores (total creativity $89.9$ vs. $81.0$ for leading alternative pipelines) (Venkatesh et al., 7 Apr 2025).

2.3 Design Workflows and Mixed-Initiative Human-AI Collaboration

Design-oriented CreAgentive systems shift the locus of agency between human and AI across multiple axes:

• Authority Distribution Framework: Five axes—Cognitive Complexity, Degree of Collaboration, Creative Agency, Responsibility, Involvement—quantify the evolving human-agent relationship for each task or workflow phase (Wadinambiarachchi et al., 25 Sep 2025).
• Multi-Agent Roles: Work coordinator, resource steward, guardian, reframer, and creative catalyst agents constitute a managerial suite, dynamically switching between passive coordination and assertive generative contributions per the five-axis settings.
• Intent Encoding: Designer intentions can be formalized as structured vectors $\mathbf{I}$ and authority weights $\mathbf{W}$, enabling real-time adaptive calibration of system autonomy within a reinforcement/planning framework (Wadinambiarachchi et al., 25 Sep 2025).

3. Underlying Computational Models and Algorithms

3.1 State and Memory Dynamics

CreAgentive agents leverage hierarchical state representations, from low-level perceptual latent spaces through structured object–affordance graphs to temporal abstractions:

• Graph-Structured Memory: Episodic working memory (WM) stores recent scene graphs, while long-term memory (LTM) persists prototypical episode clusters. Reflective reasoning retrieves high-similarity graphs from LTM for action guidance; blending operations synthesize novel concepts when standard retrievals stagnate (Catarau-Cotutiu et al., 2022).
• Reinforcement Learning Pipelines: Policy networks ingest graph-augmented state representations. Standard actor-critic objectives adapt to the composite, memory-augmented inputs, optimizing over environment rewards and internal creativity proxies (Catarau-Cotutiu et al., 2022).

3.2 Multi-Agent Workflow Orchestration

• Standardized Interfaces and Modular Layers: HAWK's five-layer architecture with 16 interfaces—User, Workflow, Operator, Agent, Resource—facilitates modular agent deployment, adaptive scheduling, and compositional resource management in CreAgentive systems (Cheng et al., 5 Jul 2025).
• Scheduling and Feedback Optimization: Subtask weights $w_\tau(t)$ are dynamically updated based on run-time utilization, with objective functions maximizing task completion under resource capacity constraints and minimizing retry loops (Cheng et al., 5 Jul 2025).

3.3 Evaluation, Quality, and Diversity Metrics

Narrative and creative outputs are tracked via hierarchical metrics (coherence, creativity, empathy, surprise, complexity, immersion) and length indicators, aggregated into QLS and robustness measures. Visual generation benchmarks involve CLIP similarity, LPIPS diversity, and human/LLM-judge scores (Cheng et al., 30 Sep 2025, Venkatesh et al., 7 Apr 2025).

4. Empirical Findings and Comparative Results

CreAgentive Application	Dimension	Metric/Result	Reference
Narrative Generation	QLS (Quality-Length)	4.78 (human: 4.96), 2,700 chapters, $\sigma \sim 0.05$	(Cheng et al., 30 Sep 2025)
Image Editing/Gen	LLM-Judge creativity	89.9 (ours) vs. 81.0 (strongest baseline)	(Venkatesh et al., 7 Apr 2025)
Narrative Engine	Cost efficiency	$<\$$1 per 100 chapters (Qwen3-30B:$0.0144$/chapter)	(Cheng et al., 30 Sep 2025)
Multi-agent planning	Throughput	5 parallel stories—no observed loss	(Cheng et al., 5 Jul 2025)

Qualitative robustness is observed in per-chapter narrative stability, resistance to degenerate collapse (baselines fail after 8 chapters), and adaptability across genres and modalities (Cheng et al., 30 Sep 2025). In creative leadership modeling, leader inventiveness carries strong impact only under low peer creativity, and dynamic modulation of conceptual change optimizes idea fitness (Leijnen et al., 2010).

5. Applications, Limitations, and Future Directions

5.1 Domains

• Narrative Content: Coherent, long-form, cross-genre literary works and scripts (Cheng et al., 30 Sep 2025, Cheng et al., 5 Jul 2025).
• Visual Art and Design: Autonomous, critiqued, and iteratively enhanced creative image workflows (Venkatesh et al., 7 Apr 2025).
• Co-Creative Game and System Design: Abstract, controllable agents for state-machine-based game mechanics (Agarwal et al., 2023).
• Robotic Planning, Interactive Assistants: Out-of-distribution generalization via affordance blending and reflective reasoning (Catarau-Cotutiu et al., 2022).

5.2 Limitations

• Domain Adaptation: Heuristic and prototype-tuned agent workflows require further generalization for specialized genres or task domains.
• LLM Dependence: Output style and fluency sensitive to backbone model quality and prompt engineering (Cheng et al., 30 Sep 2025, Cheng et al., 5 Jul 2025).
• Scalability: Graph-matching and blending complexity grows with LTM size; runtime scheduling must balance resource bottlenecks.
• Hallucination and Consistency: LLM-driven modules are susceptible to planning or logic errors, necessitating further safety and constraint integration (Cheng et al., 5 Jul 2025).

5.3 Open Directions

• Branching and Non-linear Narratives: Extension from linear to branching/interactive fiction.
• Multimodal Integration: Incorporation of visual, audio, and symbolic modalities into story and art prototypes.
• Human–AI Co-Innovation: Embedded expert feedback loops for shared agency.
• Automated Retrieval and Blending Policies: RL-driven or meta-learned memory and concept recombination (Sato, 25 Dec 2024, Catarau-Cotutiu et al., 2022).
• Hybrid Symbolic-Neural Blending: Structural semantic guarantees in agentic concept recombination (Catarau-Cotutiu et al., 2022).

6. Theoretical and Design Implications

The CreAgentive paradigm operationalizes a shift from generative to agentic AI, foregrounding persistent state, reflection, memory, blended concept synthesis, and adaptive workflow structuring. In design settings, CreAgentive is positioned as a negotiation of authority, responsibility, and generative agency, instrumented through explicit multi-axis frameworks and multimodal intent encoding (Wadinambiarachchi et al., 25 Sep 2025). The intertwined threads of analogical reasoning, intrinsic motivation diversity, and transparent, loggable state evolution represent key design principles summarized in the technical blueprints of GAI and AIGenC (Sato, 25 Dec 2024, Catarau-Cotutiu et al., 2022).

In summary, CreAgentive comprises the methodologies, system architectures, and empirical strategies for constructing AI agents—and agent collectives—capable of sustained, verifiable creative innovation in text, visual, design, and interactive domains, grounded in stateful, collaborative, multi-modal agentic workflows.