Multi-Agent.

Multi-agent systems represent a paradigm shift in AI application design, where instead of relying on a single large language model to handle all aspects of a task, multiple specialized agents work together in coordinated workflows. Each agent typically has a defined role, expertise area, or perspective, allowing the system to leverage diverse capabilities and approaches. This architecture matters because it can significantly improve output quality, reduce hallucinations through cross-validation, and handle complex tasks that require multiple types of reasoning or domain knowledge.

These systems operate through various interaction patterns, including sequential workflows where agents build upon each other's outputs, parallel processing where multiple agents tackle different aspects simultaneously, and adversarial setups where agents critique and refine each other's work. Key distinctions include the level of agent specialization, communication protocols between agents, and coordination mechanisms. Some implementations use identical base models with different prompts or fine-tuning, while others combine different model architectures optimized for specific tasks like reasoning, fact-checking, or creative generation.

Multi-agent approaches excel in scenarios requiring high reliability, complex reasoning, or creative problem-solving, but come with increased computational costs and latency. Common misconceptions include assuming that more agents always yield better results, when in practice the optimal number and configuration depend heavily on the specific use case. Organizations must balance the improved output quality against higher inference costs and longer processing times, making multi-agent systems most valuable for high-stakes applications where accuracy and thoroughness justify the additional overhead.