Evaluating Single-Agent vs. Multi-Agent AI for Mars Rover Decision Support

Type 3 Innovations is pleased to announce the publication of our latest peer-reviewed research in the Frontiers in Robotics and AI:

OpenAI Single-Agent LLM Architecture Reduces Computational Overhead Relative to Multi-Agent Orchestration in a Simulated Mars Rover Decision-Support Benchmark

Future Mars missions will increasingly rely on intelligent decision-support systems capable of interpreting terrain, rover telemetry, environmental conditions, and mission objectives while operating under significant communication delays with Earth. As AI systems become more capable, an important question emerges: does adding multiple specialized AI agents actually improve decision-making, or does it simply increase computational cost?

To investigate this question, we developed a controlled benchmark consisting of 100 mission-inspired Mars rover scenarios and evaluated both single-agent and multi-agent orchestration architectures using OpenAI GPT-4o and GPT-5.5. The benchmark was designed to isolate architecture-level performance by separating evaluation labels from the information provided to the models, enabling objective comparisons across decision quality, hazard identification, latency, token consumption, and statistical significance.

Our findings suggest that while multi-agent orchestration produced broader hazard observations, it did not consistently improve decision quality over a well-designed single-agent architecture. The most consistent difference between the approaches was computational efficiency, with the single-agent architecture requiring substantially less latency and fewer tokens across the evaluated scenarios.

Rather than assuming that more agents automatically produce better outcomes, this research demonstrates the importance of evaluating AI architectures based on measurable operational trade-offs. For short-context decision-support tasks where all relevant information is available in a single prompt, multi-agent orchestration should be considered a design decision with associated computational costs rather than a default best practice.

Beyond the specific findings, this work contributes a reproducible benchmark for studying AI architecture decisions in mission-inspired environments. We hope it serves as a foundation for future research into trustworthy, efficient, and explainable AI for autonomous systems, space robotics, and other mission-critical applications.

Read the full publication:

https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2026.1877762/full