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Agentic AI: Single vs Multi-Agent Systems
- Agentic AI involves programming with natural language through large language models (LLMs) for automating tasks, allowing for more dynamic decision-making.
- LLMs serve as a communication layer on top of structured systems and data sources, interpreting natural language but not inherently validating facts.
- Agentic AI excels in interpreting nuanced language for tasks such as customer service and research but may not be ideal for structured tasks like precise calculations.
- LangGraph, Agno, Mastra, and Smolagents are agentic AI frameworks worth exploring, with LangGraph being a popular choice among developers for building workflows.
- Single-agent workflows involve one LLM accessing multiple tools to make decisions, while multi-agent workflows distribute tasks among different agents, offering more control and precision.
- Single-agent setups are easier to start with but may lack precision for complex tasks, while multi-agent systems require careful architecture design for effective data flow and collaboration.
- Using cheaper LLMs for most agents in a multi-agent system and reserving more advanced models for crucial tasks can help optimize costs and performance.
- Building multi-agent workflows requires thoughtful architecture and data flow planning, with each agent responsible for specific tasks and interactions among different agents.
- Improvements such as parsing user queries into structured formats, ensuring agents use tools effectively, enhancing summarization, handling errors, and implementing long-term memory can enhance workflow efficiency.
- State management, particularly isolating short-term memory for each team or agent, is crucial for optimizing performance and cost in agentic systems.
- Exploring different agentic workflows, such as single vs. multi-agent setups, can offer insights into the level of control, precision, and complexity achievable in automating tasks with AI.
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