AI & TECHNOLOGY / PRACTICAL GUIDE
AI Agents Explained: Tools, Memory and Autonomy
Learn what people mean by AI agents and why permissions and checkpoints matter.
The short answer: An AI agent is a system designed to pursue a goal through multiple steps, often using tools, stored context and feedback from its environment. The term covers many designs, so ask what the system can actually observe and change.
Model, workflow and agent
A model produces an output. A workflow connects predetermined steps. An agent may choose among actions based on intermediate results. Products often combine all three.
Tools create consequences
Search, code execution, email, purchasing and file access turn text generation into actions. Each tool needs a clear permission boundary and audit trail.
Memory needs a definition
Memory may mean the current conversation, a database of past events or a user profile. Ask what is stored, for how long and who can access it.
Add checkpoints by impact
Low-risk reversible steps can be automated more freely. Spending, publishing, deletion and sensitive-data actions should have explicit controls and human review.
Put it into practice
Try this: Draw a permission map for one agent: what it can read, write, spend, publish and delete. Add a human checkpoint before every consequential action.
Sources and further reading
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Educational purpose: This guide provides general education. It does not provide personalised financial, investment, legal or tax advice.