Survey Maps Agentic AI Architectures and LLM Agent Taxonomies

A comprehensive new survey paper has emerged from the research community, systematically mapping the landscape of agentic artificial intelligence architectures, taxonomies, and evaluation methodologies for large language model (LLM) agents. This foundational work arrives at a critical moment when AI agents are rapidly transitioning from research concepts to production systems across industries.

The Rise of Agentic AI Systems

The survey addresses one of the most significant shifts in AI development: the evolution from reactive, query-response systems to autonomous agents capable of planning, reasoning, and executing multi-step tasks. Unlike traditional LLMs that simply generate text responses, agentic AI systems can interact with tools, maintain persistent memory, decompose complex goals into subtasks, and adapt their strategies based on environmental feedback.

This architectural evolution has profound implications for synthetic media and content creation workflows. Agentic systems are increasingly being deployed to orchestrate complex video generation pipelines, manage multi-modal content creation, and even coordinate deepfake detection systems that must autonomously identify and flag manipulated media at scale.

Architectural Frameworks Examined

The survey provides a systematic categorization of agent architectures, examining how different components interact to enable autonomous behavior. Key architectural patterns include:

Single-agent architectures where a monolithic LLM handles all reasoning, planning, and action selection. These systems benefit from simplicity but may struggle with complex, multi-domain tasks.

Multi-agent systems where specialized agents collaborate on different aspects of a task. For example, in video generation workflows, one agent might handle script generation, another manages visual composition, and a third oversees quality control and consistency checking.

Hierarchical architectures featuring meta-agents that coordinate subordinate agents, enabling sophisticated task decomposition and resource allocation. These structures mirror how human creative teams operate, with directors, editors, and specialists each contributing expertise.

Taxonomies for Classification

The research introduces rigorous taxonomies for classifying agent capabilities and behaviors. These classification systems help researchers and practitioners understand where specific agent implementations fit within the broader landscape and what capabilities they might be missing.

The taxonomies address several dimensions including autonomy levels (from fully supervised to fully autonomous operation), reasoning capabilities (reactive, deliberative, or hybrid), memory architectures (episodic, semantic, or working memory configurations), and tool integration patterns (how agents interact with external APIs, databases, and computational resources).

For the synthetic media space, these taxonomies provide valuable frameworks for understanding how AI video generation tools might evolve. Current systems like Runway, Pika, and others operate primarily as tool-augmented generators, but the trajectory toward more autonomous agentic systems is clear.

Evaluation Methodologies

Perhaps the most practically valuable contribution is the survey's examination of evaluation frameworks for LLM agents. Assessing agentic systems presents unique challenges compared to traditional LLM benchmarks—agents must be evaluated not just on output quality but on their planning efficiency, error recovery, resource utilization, and alignment with user intentions.

The survey catalogs existing benchmarks and proposes evaluation criteria spanning multiple dimensions:

Task completion metrics measuring whether agents successfully achieve stated goals. Efficiency metrics tracking computational resources, time, and number of steps required. Robustness evaluations testing how agents handle unexpected situations, errors, and adversarial inputs. Safety assessments examining whether agents stay within defined operational boundaries.

Implications for Digital Authenticity

The frameworks presented have direct relevance to digital authenticity and deepfake detection systems. As synthetic media generation becomes more sophisticated, detection systems increasingly require agentic capabilities—autonomous operation at scale, integration with multiple detection tools, adaptive strategies for novel manipulation techniques, and coordinated responses across platforms.

Understanding agent architectures helps authenticity researchers design more effective detection pipelines. A hierarchical multi-agent approach, for instance, might coordinate specialized detectors for different manipulation types while a meta-agent synthesizes findings and makes final determinations.

Looking Forward

The survey acknowledges significant open challenges in the field, including the need for more robust evaluation benchmarks, better understanding of emergent agent behaviors, and improved techniques for ensuring agent safety and alignment. As these systems become more capable, ensuring they operate within intended boundaries becomes increasingly critical.

For practitioners in AI video generation and synthetic media, this survey provides an essential reference for understanding where current systems fit within the broader agent landscape and what architectural patterns might enable next-generation capabilities. The systematic taxonomies and evaluation frameworks offer concrete guidance for both building and assessing agentic AI systems.

Stay informed on AI video and digital authenticity. Follow Skrew AI News.