How do I choose the right agent architecture for my use case?

Match complexity to the problem. If the task is predictable with clear rules, a simple reflex agent works. If it requires tracking state over time, go with a model-based approach. If it is open-ended and needs dynamic reasoning, use an agentic loop. If it is too large or too diverse for a single agent, go multi-agent. Start with the simplest architecture that solves the problem. You can always add complexity later.

How is an AI agent different from a traditional chatbot?

A chatbot responds to individual inputs. An agent plans multi-step sequences, uses external tools, observes results, and adjusts. The difference is autonomy: agents figure out how to reach a goal, while chatbots answer the question at hand.

What is the ReAct pattern in AI agents?

ReAct (Reasoning + Acting) alternates between thinking and doing. The agent reasons through one step, executes it, observes the outcome, and uses that to decide the next step. This makes it adaptive to unexpected results, which static workflows can't handle.

When should I use a multi-agent system instead of a single agent?

Only use multiple agents when the task exceeds a single agent's context window, skill set, or reliability requirements. Single-agent loops are simpler, cheaper, and easier to debug. Multi-agent coordination overhead is real. Avoid it unless the problem truly requires it.

What role does MCP play in agent architecture?

Model Context Protocol (MCP) is an open standard that lets agents connect to external tools and data sources in a universal way. Rather than building custom integrations for each framework, you connect to an MCP server once, and any compatible agent can use it. Think USB-C for agent tooling.