Agentic

Your agent makes a tool call. The tool returns 40 rows of JSON. That JSON gets stuffed back into the model’s context, the model thinks for a bit, calls another tool, gets another 40 rows back, and on it goes. By iteration 10, your prompt is mostly curly braces, quoted keys, and the same field names repeated hundreds of times. You’re paying for every single one of those tokens. And you’re paying for them on every subsequent model call in the run, because tool results stick around in the conversation history. ...

🎯 Introduction “I’ll just throw it in DynamoDB.” I’ve heard this line dozens of times from engineers building AI agents. It sounds reasonable. You need to store conversation history, maybe some user preferences. DynamoDB is fast, scalable, and you already use it. How hard could it be? Here’s the thing: agent memory isn’t just storage. It’s what separates an agent that forgets everything you said five minutes ago from one that actually remembers your last conversation, pulls up context from three weeks ago, and knows which details matter and what data is fresh. ...