Agent State, Memory & Checkpointing: Three Things That Sound Similar but Aren’t

<p>Most agent frameworks treat memory as an afterthought. They give your agent tools, prompts, and orchestration patterns — but when you restart the conversation, everything

<p>`---<br /> title: "Why I Built a Brain for My AI Agents — and What It Taught Me About Memory"<br /> published: true<br /> description: "Anamnesis v0.3.0: The cognitive

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<p>This post originally appeared on <a href="https://tokenjam.dev/blog/2026-05-13-agent-memory?utm_source=devto&amp;utm_medium=referral&amp;utm_campaign=cross-post" rel="noopener

<p>Most AI agents have amnesia.</p> <p>You close the chat window. They forget everything. Every session starts from zero. Every context window fills up and dumps the earliest

<p>Every agent pipeline I've touched in the last eighteen months reinvents memory, and most of them do it badly.</p> <p>Planner decisions never reach the executor. Giant prompts

<p>You've heard of DevOps. You've heard of AIOps. You've probably heard of MLOps.</p> <p>But there's a category that doesn't exist yet — and it's the one the AI industry needs

<p>The easiest way to make an LLM application look smart is to give the model more context.</p> <p>The harder problem is deciding what context deserves to be there.<br /> While