---
title: "AI agents and memory explained | Snowplow Signals"
description: "Giving an agent the context and memory to act on what a customer just did."
source: https://signals.snowplow.io/learning/ai-agents-and-memory
---

Learning Center

# AI agents and memory

AI agents are only as good as what they know. These explainers cover agent memory and context, how they differ, and why real-time context is what lets a customer-facing agent act on what someone is doing right now.

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## In this topic

[What is agent memory?Agent memory is the information an AI agent keeps so it does not start from nothing on each turn. It spans what happened moments ago in the current task and what the agent learned in past sessions.](https://signals.snowplow.io/learning/ai-agents-and-memory/what-is-agent-memory) [Short-term vs long-term agent memoryShort-term memory holds the current task and is discarded when it ends. Long-term memory persists across sessions. The two solve different problems, and most useful agents combine them.](https://signals.snowplow.io/learning/ai-agents-and-memory/short-term-vs-long-term-agent-memory) [What is agentic context?Agentic context is the structured picture of the current situation that an AI agent reads before it acts. It is how the agent knows what is going on beyond the words of the prompt.](https://signals.snowplow.io/learning/ai-agents-and-memory/what-is-agentic-context) [Context engineering vs prompt engineeringPrompt engineering is about wording the instruction well. Context engineering is about assembling the right information for the agent to read. For agents that act in the real world, the context usually matters more than the phrasing.](https://signals.snowplow.io/learning/ai-agents-and-memory/context-engineering-vs-prompt-engineering) [Why AI agents need real-time contextAn AI agent is grounded by the context it reads. Hand it a stale snapshot and it responds to a situation that has already moved on. Real-time context keeps the agent aligned with what the user is doing right now.](https://signals.snowplow.io/learning/ai-agents-and-memory/why-ai-agents-need-real-time-context) [What is a customer-facing AI agent?A customer-facing AI agent interacts with your users directly, in support, sales, shopping or onboarding. Unlike an internal copilot, it acts in front of the customer, so what it knows about them decides whether it helps.](https://signals.snowplow.io/learning/ai-agents-and-memory/what-is-a-customer-facing-ai-agent) [How agents act on live customer behaviorContext and memory are means to an end, which is action. An agent that reads what a customer is doing now, and remembers what they did before, can respond in the moment rather than after the fact.](https://signals.snowplow.io/learning/ai-agents-and-memory/how-agents-act-on-live-customer-behavior) 

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