hardAnthropic OpenAI Sierra

Design the memory architecture for an agent that runs for weeks across thousands of interactions.

A context window is not memory. Real agents need a storage architecture: working buffer, episodic recall, and consolidated facts. The signal is the read/write paths and how you keep memory from rotting.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Agent Engineering·Memory and contextSign in13mMemory is four problems wearing one wordDiscussions about agent memory go in circles because four different problems share the name. Separating them turns an unbounded design question into four bounded ones, each with a known answer.Agent Engineering·Memory and contextSign in14mState that has to survive the processOnce a run can pause for approval, be retried, or outlive a deployment, its progress has to live somewhere durable. This lesson covers what to checkpoint, why steps must be safe to repeat, and how retrieval changes when an agent is the one searching.

UP NEXT ON YOUR JOURNEY

Next in this trackHow should an agent recover from tool errors: retries, backoff, and when to give up?Next in this trackWhat protocols govern how agents hand off work, and what makes multi-agent coordination break?Next in this trackBeyond basic tools, what advanced MCP patterns matter: resources, prompts, sampling, and roots?

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