Loading lesson…
Your agent forgets between sessions unless you give it actual memory — not just a longer context window.
Context = what's in this chat right now. Memory = stored notes your agent reads back next time.
Set up a tiny agent with a memory.md file it reads at start and writes to at end.
Understanding "Memory vs context window: what your agent remembers" in practice: AI agents don't just answer questions — they can do things, like looking things up, writing files, or talking to apps. Your agent forgets between sessions unless you give it actual memory — not just a longer context window — and knowing how to apply this gives you a concrete advantage.
15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-builders-agentic-ai-ai-agent-memory-vs-context-r11a8-teen
Which sentence best captures the main idea of 'Memory vs context window: what your agent remembers'?
Which of the following is part of 'Some examples'?
Which of the following is part of 'Heads up'?
Which of the following is part of 'You did it!'?
What is 'context window' in this context?
What is 'persistent memory' in this context?
Which statement best contrasts memory and context?
Why is it dangerous to give an agent access to your email and calendar without scoped permissions?
What should an agent's trace let you do after a run?
Which signal best tells you an agent is stuck in a runaway loop?
What is the most reliable way to keep an autonomous agent from going off the rails on a long task?
Which budget control most directly prevents runaway costs from an agent loop?
Which is the best way to think about an agent's 'autonomy level'?
Which is the clearest sign an 'agent' is really just a chatbot in disguise?
An agent that costs $0.04 per task on average will run 10,000 times this month. Roughly what should you budget?