In the field of artificial intelligence, code agents have emerged as a key tool for tackling complex visual and logical reasoning tasks, such as those posed by the ARC-AGI-3 benchmark. However, a fundamental question arises: do these agents really need to incorporate executable world models to achieve optimal performance? A recent attribution study based on four variants of Codex agents sheds light on this issue. Results show that while complete verification with exact reproduction of observations —an approach requiring a persistent executable model— achieved the best performance across all evaluated settings, the differences between variants were smaller than expected. In fact, in some cases, the textual variant without an executable model outperformed the flexible-interface variant. This suggests that the need for an executable model largely depends on the context and the underlying model's capability.
For businesses developing AI solutions, this research has direct implications. The choice between approaches should not be dogmatic but rather aligned with available resources and specific objectives. For example, in tasks where execution fidelity is critical —such as control or simulation systems— an executable model with verification may be indispensable. Conversely, for analysis or report generation tasks, a well-tuned textual agent can be more computationally cost-effective.
In this context, Q2BSTUDIO, as a company specialized in software and technology development, offers custom software that integrates AI agents tailored to each business's specific needs. Our team carefully evaluates whether implementing executable models or purely textual approaches is more suitable for each case, optimizing the balance between performance and resource consumption. Additionally, we combine these capabilities with cloud AWS/Azure services to scale solutions securely and efficiently.
Cybersecurity also plays a crucial role when code agents interact with production systems. An agent that executes actions based on real-world observations can be vulnerable to attacks if not properly protected. Therefore, at Q2BSTUDIO, we integrate cybersecurity as a fundamental part of the development lifecycle, ensuring agents are robust against malicious manipulation.
Another relevant aspect is the ability of agents to analyze large volumes of data and generate business intelligence reports. The study's findings indicate that as underlying models become more powerful —as seen with the gpt-5.6-sol versions in follow-up experiments— the difference between approaches narrows. This opens the door to integrating AI agents with BI / Power BI tools, enabling businesses to automate data analysis and decision-making without needing complex executable models.
In summary, the question of whether code agents need executable models for ARC-AGI-3 does not have a single answer. The study demonstrates that complete verification delivers the best performance, but at a higher cost, while textual approaches can be sufficiently effective in many scenarios. For businesses, the key is to carefully analyze their requirements and choose the most suitable architecture with the support of technology partners like Q2BSTUDIO. We offer customized AI services, as well as process automation, so each organization can fully leverage code agent capabilities without compromising efficiency or security.
