# Beyond Manual Media Coding: Evaluating Large Language Models and Agents for News Content Analysis
## 2 Related Work
### Coding with Autonomous Agents  
An agent can be defined as a system \"that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators\" [27]. Additionally, the concept of a rational agent describes an agent that selects an action for each percept sequence such that the expected outcome maximizes its performance measure, based on the available perceptual evidence and the agent's prior knowledge [27]. This definition of an agent, as one that utilizes environmental input, draws on prior knowledge, and employs tools to achieve a goal, aligns well with recent LLM-based agent architectures. However, while early agentic approaches were constrained by limited capabilities in natural language understanding and generation, recent advancements in LLMs have enabled the development of intelligent agents that can collaborate to accomplish tasks of high cognitive complexity. The ability of LLM-based agents to increase their accuracy through collaborative reasoning and the utilization of prior and in-context knowledge and tools mitigates some of the inherent LLM limitations. This enables LLM-based agents to excel in a variety of tasks including content understanding and reasoning [28; 29], software engineering [30; 31; 32; 33], societal simulations [34; 35; 36], economic modeling [37; 38], and robotics [39; 40; 41].  
In recent LLM-based agentic architectures, agents leverage profiling, memory mechanisms, planning modules, and a predefined set of actions to accomplish specified tasks [26]. The _profiling module_ defines agent roles and characteristics using handcrafted prompt templates, LLM-generated descriptions, or automated alignment with task-specific datasets, and therefore establishes a contextual persona foundation that guides all subsequent agent behaviors [34; 42]. The _memory module_ stores environmental perceptions and past experiences through memory architectures that integrate short-term contextual awareness or long-term knowledge storage. This enables the utilization of memory operations, such as reading, writing, and updating, thus allowing behavioral modeling, situational aware ness, and task-oriented adaptation over time [43; 44]. The _planning module_ facilitates task decomposition and multi-agent planning utilizing reasoning strategies, such as CoT [18], Tree-of-Thought (ToT) [45], and Reasoning and Acting (ReAct) [46], which allow LLM agents to incorporate feedback from the environment and human users to iteratively refine execution plans. Finally, the _action module_ translates agent decisions into executable actions by following plans, retrieving relevant memories or utilizing external systems. It employs both external tools (e.g., APIs, databases, or specialized models) and internal LLM capabilities (e.g., planning, dialogue generation, and commonsense reasoning) to carry out tasks effectively [31; 47; 48].  
The application of agentic workflows on structured news coding-based analysis remains under-explored. Recent work applies multi-agent LLM systems to perform large-scale thematic analysis, focusing on exploring diverse qualitative themes through internal consistency metrics [49]. In contrast, we conduct a structured content analysis using a comprehensive, manually developed codebook and human-annotated ground truth, enabling a direct evaluation of LLM coding capabilities, from direct responses and multi-agent teams, in domain-specific analytical questions.  
Manual media coding involves sophisticated, multi-step workflows, from codebook preparation to content understanding, question answering, error analysis, and collaborative iterative refinement, which are, in effect, a representation of an agentic system. These processes embody the core principles of agentic AI: breaking down a complex goal into a plan, using tools, and reflecting on feedback and collaboration to improve performance. This points toward a research frontier focused on formalizing these processes into more autonomous agentic workflows, which is the central focus of this study.