Design & Architecture
- How should I design the architecture of an AI agent?
Start with a clear goal, then design modular components: perception, reasoning, memory, and action. Use a loop that observes, thinks, and acts, with feedback for improvement. - What is the ReAct pattern and why use it?
ReAct (Reason + Act) is a pattern where the LLM alternates between reasoning steps and actions (like tool calls). It improves accuracy and transparency by letting the model think before acting. - How do I add memory to an AI agent?
Add short-term memory via the LLM's context window and long-term memory using a vector database to store and retrieve relevant information. Summarize or embed past interactions for efficient recall. - What is the difference between a single-agent and multi-agent system?
A single-agent system uses one AI agent to handle all tasks, while a multi-agent system uses multiple specialized agents that collaborate or compete to solve problems. Multi-agent systems can be more scalable and robust but are harder to coordinate. - How do I handle tool use and function calling in AI agents?
To handle tool use, define clear function schemas, let the agent decide when to call them, and execute the calls in a sandboxed environment. Return results to the agent for further reasoning, and handle errors gracefully. - What are common pitfalls in AI agent design?
Common pitfalls include unclear goals, poor error handling, overcomplicating with multi-agent systems, ignoring costs, and failing to test thoroughly. Agents can also get stuck in loops or take unsafe actions.