Multi-agent systems have become a cornerstone in advancing complex AI applications, enabling solutions to problems that single agents cannot handle alone. From continuous software development to in-depth market research and intelligence reporting, these systems thrive on structured collaboration, dynamic decision-making, and integration with custom tools like REPLs, plotting interfaces, and web search. One of the most critical lessons learned is the importance of using high-capacity language models for supervisory roles. Lower-parameter models, especially those under 7B parameters, struggle with maintaining consistent output formats such as JSON, XML, or Markdown, often leading to repeated agent calls and poor task progression. A powerful model like a 32B parameter variant distilled from DeepSeek-R1 significantly outperforms smaller models in both reasoning and structural enforcement. Supervisors must have access to complete interaction history. Early systems that provided minimal context—only recent outputs and basic instructions—failed to make informed decisions. In contrast, systems that feed the supervisor with a full task history enable better planning, clearer agent selection, and more reliable workflow management. This creates a natural division of labor: the supervisor handles strategy and coordination, while agents focus on execution. Another key insight is the necessity of clear, concrete goals. Ambiguous instructions lead to agent loops—endless cycles where agents keep refining the same task without progress. Defining explicit success and failure conditions prevents this. For example, instead of asking to “improve the text,” specify “reduce the text by 30% while preserving all key points, verified against a pre-defined list.” This clarity ensures agents know when to stop. To avoid overloading supervisors, it’s better to separate roles. Use a supervisor for workflow management and a dedicated advisor for quality evaluation and strategic feedback. This “second brain” model enhances decision-making, suggests optimizations, and catches potential errors early. Hierarchical structures mirror real-world organizations and work exceptionally well. A two-layer system—chief supervisor → team supervisors → expert agents—enables specialization, accountability, and scalable coordination. For instance, in equity research, a senior model delegates tasks like data collection, modeling, and reporting to specialized team leads, each managing their own sub-agents. This structure improves accuracy, reliability, and efficiency. Start simple. Begin with a single supervisor managing 3–5 specialized agents. As complexity grows, evolve high-workload agents into structured teams. Keep the span of control small—no more than five agents per supervisor—to maintain clarity and responsiveness. Leverage complementary models. Pairing models with different strengths—such as Claude Opus 4.1 for precision and GPT-5 for creativity—can dramatically improve performance. Assign tasks based on model capabilities: use one for strict adherence to format, another for innovative reasoning. Long-term memory enhances output diversity. When models can reference past work, they naturally avoid repetition and generate more varied, original ideas. This can be implemented through persistent memory banks that store prior outputs and context, enabling richer, more dynamic generation. When choosing a framework, consider your needs. LangGraph offers fine-grained, graph-based orchestration with strong debugging tools and production readiness—ideal for complex, stateful workflows. LlamaIndex excels in integrating private data and enabling retrieval-augmented generation, making it perfect for knowledge-intensive applications. CrewAI provides role-based agent collaboration with built-in memory and hierarchical execution. AutoGen shines in dynamic, conversational development and rapid prototyping, though debugging can be challenging. For advanced users, building without a framework offers maximum control and performance. Many production systems eventually move to custom implementations using direct API calls and modular components like FAISS or Qdrant for vector storage. A hybrid approach—using frameworks for specific tasks like data loading or chunking, but managing agents and logic directly—is common and effective. Ultimately, building successful multi-agent systems requires balancing structure with flexibility. Focus on clear goals, strong supervision, role separation, and intelligent model selection. As the field evolves, self-organizing systems that adapt dynamically to task demands may represent the next frontier. These principles—limited scope, comprehensive context, concrete goals, hierarchy, model synergy, and memory—form the foundation of robust, scalable, and high-performing multi-agent architectures.