Question 1

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

Accepted Answer

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.

Question 2

How is an AI agent different from a traditional chatbot?

Accepted Answer

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.

Question 3

What is the ReAct pattern in AI agents?

Accepted Answer

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.

Question 4

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

Accepted Answer

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.

Question 5

What role does MCP play in agent architecture?

Accepted Answer

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.

AI Agent Architecture: How Agents Actually Work (and Why It Matters)

AI Agent Architecture: How Agents Actually Work (and Why It Matters)

Related articles

Why We're Open Sourcing OpenUI

What Is an AI Agent Builder, How It Works, & How to Build One

AI Copilots: A Complete Guide to Intelligent Assistance

What Are AI Agents: Types, Capabilities, and Real-World Use

Top LLMs 2025: Understanding the Models Behind Modern AI Systems

ChatKit: What It Is, What It Isn’t, and Where C1 Fits In

PowerPoint Automation Reimagined with C1 Artifacts API

The Future of AI Interfaces is Generative

MCP UI: Making AI Apps Interactive

What Is AG-UI and Why It Matters for AI-Native User Interfaces

How to design AI-Native Conversational Interfaces : From Templates to Generative UI

GPT 5 vs. GPT 4.1

How to Build Generative UI Applications

Implementing Generative Analytics with Thesys and MCP

Evolution of Analytics: From Static Dashboards to Generative UI

Why Generating Code for Generative UI is a bad idea

Building the First Generative UI API: Technical Architecture and Design Decisions Behind C1

How we evaluate LLMs for Generative UI

Generative UI vs Prompt to UI vs Prompt to Design

Generative UI: What It Is and Why It Matters