AI Agent Architecture: Models, Tools, Memory, Workflows, and Guardrails
Learn how models, tools, memory, workflows, permissions, guardrails, human oversight, and evaluation together form a practical AI agent system.
AI Agents and Goal-Driven Systems
AI agents do more than generate text. This cluster explains how models use tools, retain context, complete steps, and operate within limits that people can inspect and control.
4 articles
A practical route through the AI agent system
Four guides separating architecture, system choice, human oversight, and action guardrails.
- 01
AI agent architecture from models to environments — Begin with tools, state, memory, loops, permissions, guardrails, and evaluation.
- 02
the difference between agents, chatbots, and workflows — Choose the simplest system sufficient for the outcome and its risk.
- 03
placing people at the right checkpoints — Separate input, approval, exceptions, takeover, review, and approval fatigue.
- 04
constraining capabilities, permissions, autonomy, and impact — Enforce controls through execution, environments, monitoring, and recovery.
Start here
Topic map
Choosing the system form
Distinguish chatbots, workflows, agentic workflows, and agents by action and flexibility.
- AI Agent vs Chatbot: What Is the Difference?
Compare AI agents and chatbots across goals, tools, autonomy, memory, actions, risks, and the business situations suited to each.
Human oversight
Place human judgement and authority at boundaries proportionate to risk.
- Human-in-the-Loop for AI Agent Systems
Understand human-in-the-loop for AI agents: approvals, escalation, review, approval fatigue, risk tiers, and effective oversight design.
Guardrails and action security
Constrain tools, identity, permissions, execution, blast radius, and recovery when controls fail.
- Guardrails for Controlling AI Agent Actions
Learn how AI agent guardrails constrain capabilities, permissions, autonomy, data, execution, impact, and recovery when controls fail.
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